tag:blogger.com,1999:blog-87070527281837626332024-03-27T23:44:50.687-07:00Beginning of the FutureThe latest happenings from the ever growing tech world...Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.comBlogger22125tag:blogger.com,1999:blog-8707052728183762633.post-47415121634619871452021-04-04T11:15:00.002-07:002021-04-04T11:15:27.465-07:00Everything to Know About Convolutional Neural Networks<div style="text-align: left;"><div style="text-align: center;"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Convolutional Neural Networks" class="graf-image" data-height="667" data-image-id="0*El02XZwqZKArUeRL" data-width="1000" src="https://cdn-images-1.medium.com/max/1000/0*El02XZwqZKArUeRL" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: georgia; font-size: x-small;">Photo by <a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@clintadair?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" href="https://unsplash.com/@clintadair?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" rel="noopener" target="_blank">Clint Adair</a> on <a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/s/photos/neural-networks?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" href="https://unsplash.com/s/photos/neural-networks?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" rel="noopener" target="_blank">Unsplash</a></span></td></tr></tbody></table><figure class="graf graf--figure" name="a61d"><figcaption class="imageCaption"><br /></figcaption></figure></div><div style="text-align: center;"><b style="font-family: georgia;"><i>"From a computer vision point of view, there's no doubt that deep convolutional neural networks are today's "master algorithm" for dealing with perceptual data." </i></b></div><span style="font-family: georgia;"><div style="text-align: center;"><b><i> - Tomasz Malisiewicz</i></b></div><div style="text-align: center;"><b><i><br /></i></b></div><div style="text-align: justify;"><p>Nowadays, we all must have seen and used various effects and filters on images and how our computers and smartphones detect and recognize faces in photographs and videos. These all things are possible by "computer vision" which is nothing but machine learning using convolutional neural networks.</p><p>Computer vision is similar to human vision, it helps the system to recognize, classify, detect complex features in data. Some of its applications can be seen in self-driving cars, vision for robots, facial recognition.</p><p><b><i>But this computer vision is not completely the same as our human vision, unlike us the computer sees the image in the form of a matrix of pixels.</i></b></p><p>An image is made up of pixels. And each pixel value can take a value from 0 to 255.</p><h2>What is a convolutional neural network</h2><p>A convolutional neural network or CNN is a kind of neural network that is used in processing data with input shape in 2D matrix form like images.</p><p>The structure of a convolutional neural network is a feed-forward with several hidden layers in the sequence mainly convolution and pooling layers followed by activation layers. With this CNN model, we can recognize handwritten letters and human faces (depending on the number of layers and complexity of the image).</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Convolutional neural network model" class="graf-image" data-height="237" data-image-id="1*9WmPbmk3UL4CjM6JNCv2Og.png" data-width="616" src="https://cdn-images-1.medium.com/max/1000/1*9WmPbmk3UL4CjM6JNCv2Og.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"><span style="text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://commons.wikimedia.org/wiki/File:ConvolutionAndPooling.svg" href="https://commons.wikimedia.org/wiki/File:ConvolutionAndPooling.svg" rel="noopener" style="text-align: justify;" target="_blank">Wikimedia Commons</a><span style="text-align: justify;">)</span><strong class="markup--strong markup--figure-strong" style="text-align: justify;"> Convolutional neural network model</strong></span></td></tr></tbody></table><p class="graf graf--p" name="3e77">In this article, we will learn concepts of CNN and build an image classifier model for a better grasp of the subject.</p><p class="graf graf--p" name="0a22">Before building the model we need to understand and learn few important concepts of convolutional neural networks.</p><p class="graf graf--p" name="0a22"></p><ul><li>As we already know, computers view images as numbers in the form of a matrix of pixels. CNN views images as three-dimensional objects where height and width are the first two dimensions and color encoding is the third dimension (for example, 3x3x3 RGB images).</li></ul><div></div><p></p><p><b><i>Now just imagine, how computationally intensive it will be to process a 4K image (3840 x 2160 pixel).</i></b></p><h2>Convolution</h2><div><ul><li>So the main objective of convolutional networks is to reduce the images into the form which is easier to process while preserving the features and maintaining a good accuracy while predicting.</li></ul><div><b><i>There are three main significant units in convolutional neural networks i.e. input image, feature detector, and feature map.</i></b></div></div><div><ul><li><b>A feature detector is a kernel of filter (a matrix of numbers, usually 3x3).</b> Here the idea is to multiply the matrix representation of images, element-wise with the kernel to get a feature map. In this step, the size of the image is reduced for faster and simpler processing. Important features of the image are retained (like features that are unique to the image/object i.e. necessary for the recognition). However, some features are lost in this step.</li><li>For example, if we have an input image of 5x5x1 dimensions and the convolution kernel/filter we apply to an image is of 3x3x1 dimension:</li></ul><div><div><b style="background-color: #eeeeee;">Image matrix:</b></div><div><span style="background-color: #eeeeee;">1 1 0 1 1</span></div><div><span style="background-color: #eeeeee;">1 0 1 0 1</span></div><div><span style="background-color: #eeeeee;">1 1 1 1 0</span></div><div><span style="background-color: #eeeeee;">0 0 1 1 0</span></div><div><span style="background-color: #eeeeee;">1 1 0 0 0</span></div><div><span style="background-color: #eeeeee;"><br /></span></div><div><b style="background-color: #eeeeee;">Kernel matrix:</b></div><div><span style="background-color: #eeeeee;">1 0 1</span></div><div><span style="background-color: #eeeeee;">0 1 0</span></div><div><span style="background-color: #eeeeee;">1 1 0</span></div></div></div></div></span></div><div style="text-align: left;"><span style="background-color: #eeeeee;"><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span><span style="font-family: georgia;"></span></span><span style="font-family: georgia;"><div style="text-align: justify;"><div><div><br /></div><div>Then the convolved feature obtained after the multiplication of kernel matrix with each element of image matrix will be:</div></div><div><br /></div><div><div><b style="background-color: #eeeeee;">Convolved matrix:</b></div><div><span style="background-color: #eeeeee;">3 5 3</span></div><div><span style="background-color: #eeeeee;">3 2 5</span></div><div><span style="background-color: #eeeeee;">4 4 2</span></div></div><div><br /></div></div></span></div><div style="text-align: left;"><div style="text-align: justify;"><div style="font-family: georgia;"><div>Here, kernel shifts 9 times because <b>stride length</b> is 1 (i.e. filter will slide after each element of image matrix). </div><h2>ReLu activation function</h2><div>The purpose of applying this <b>ReLu function </b>(rectified linear unit) is to increase nonlinearity in the model. Since the image/object has several features that are not linear to each other. We apply this function so that our model does not treat image classification as a linear problem.</div></div><div style="font-family: georgia;"><h2>Pooling layer </h2><div>The pooling layer is similar to the convolutional layer, and it is responsible for the reduction of the size of the convolved matrix.</div><div><figure class="graf graf--figure" name="9c78"><div style="text-align: center;"><img alt="Pooling layer — feature map" class="graf-image" data-height="225" data-image-id="1*_UCSyZF1cdN0uo8UImbifg.png" data-width="135" src="https://cdn-images-1.medium.com/max/1000/1*_UCSyZF1cdN0uo8UImbifg.png" /></div><figcaption class="imageCaption"><span style="font-size: x-small;">(Image by <a class="markup--anchor markup--figure-anchor" data-href="https://commons.wikimedia.org/wiki/File:ConvolutionAndPooling.svg" href="https://commons.wikimedia.org/wiki/File:ConvolutionAndPooling.svg" rel="noopener" target="_blank">Wikimedia Commons</a>) <strong class="markup--strong markup--figure-strong">Pooling layer — feature map</strong></span></figcaption></figure></div><div><br /></div><div>It is an important step in the process of a convolutional neural network. Pooling is essential for <b>detecting and extracting prominent features</b> from images irrespective of different positions, angles, different lighting, etc. while maintaining the accuracy and efficiency of the training model.</div><div><br /></div><div>Furthermore, as the size of the image data is reduced (while preserving the dominant features)<b>, the computational power required to process the data is also decreased. </b></div><div><b><br /></b></div><div>There are different types of pooling: max pooling, min pooling, and average pooling.</div></div><div style="font-family: georgia;"><ul><li><b>Max pooling </b>extracts the maximum value from the portion of the feature map matrix covered by the kernel (specific pool size like 2x2). </li><li><b>Min pooling</b> extracts the minimum value from the portion of the feature map matrix covered by the kernel (specific pool size like 2x2).</li><li>While <b>average pooling</b> average of all values is selected from the portion of the feature map matrix covered by the kernel (specific pool size like 2x2).</li></ul><div>Max pooling is the most efficient of all the pooling methods (since it will contain the most dominant features of the convolutional feature map).</div><div><br /></div><div><b style="background-color: #eeeeee;">Convolved matrix:</b></div><div><span style="background-color: #eeeeee;">3 5 4 1</span></div><div><span style="background-color: #eeeeee;">2 2 5 6</span></div><div><span style="background-color: #eeeeee;">4 4 2 5</span></div><div><span style="background-color: #eeeeee;">1 3 5 4</span></div><div><span style="background-color: #eeeeee;"><br /></span></div><div><b style="background-color: #eeeeee;">Max pooled matrix:</b></div><div><span style="background-color: #eeeeee;">5 6</span></div><div><span style="background-color: #eeeeee;">4 5</span></div><div><span style="background-color: #eeeeee;"><br /></span></div><div><b style="background-color: #eeeeee;">Min pooled matrix:</b></div><div><span style="background-color: #eeeeee;">2 1</span></div><div><span style="background-color: #eeeeee;">1 2</span></div><div><span style="background-color: #eeeeee;"><br /></span></div><div><b style="background-color: #eeeeee;">Average pooled matrix:</b></div><div><span style="background-color: #eeeeee;">3 4</span></div><div><span style="background-color: #eeeeee;">3 4</span></div><div><br /></div><div>Above these are the pooled feature maps.</div><div><br /></div><div><b><i>The number of these convolution and pooling layers can be increased or decreased depending on the complexity of the input image and the level of details and features that has to be extracted. But remember the number of layer you increase in the model, the computational power required will also increase.</i></b></div><div><br /></div><div>With these convolution and pooling layers, our model can understand extract the feature of the image.</div></div><div><h2 style="font-family: georgia;">Flattening</h2><div style="font-family: georgia;">The next step is to flatten the pool feature map obtained i.e. transforming the multidimensional pool feature map matrix to a single dimension array (linear vector or column) to feed it to the neural network for processing and classification.</div><h2 style="font-family: georgia;">Full connection layer - Classification</h2><div style="font-family: georgia;">After we have obtained our data in the form of a column vector, we will pass it through the feed-forward neural network, where the backpropagation is implemented over every iteration during the process of training (accuracy of prediction is improved).</div><div style="font-family: georgia;"><br /></div><div style="font-family: georgia;"><b><i>After several epochs of training, our model will be able to recognize and distinguish between prominent and low-level features of the image.</i></b></div><div style="font-family: georgia;"><b><i><br /></i></b></div><div style="font-family: georgia;">The final output values obtained from the neural network may not sum up to one, but it is necessary to bring these values between zero and one. This will represent the probability of each class and further, classify them for the output using the<b> softmax technique (activation function used for multi-class classification).</b></div><h2 style="font-family: georgia;">Implementation of CNN using MNIST dataset</h2><div style="font-family: georgia;">In this article, we will be using the MNIST dataset i.e. a<b> dataset of 70,000 </b>(60,000 training images and 10,000 test images) <b>small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9.</b></div><div style="font-family: georgia;"><b><br /></b></div><div style="font-family: georgia;">Here the objective of our model is to classify a given set of images of handwritten digits into 1 to 10 (representing integers from 0 to 9).</div><div style="font-family: georgia;"><br /></div><div style="font-family: georgia;"><b><i>We will be using Keras and matplotlib library in this article.</i></b></div><div style="font-family: georgia;"><b><i><br /></i></b></div><div style="font-family: georgia;">The code below will load the first nine images of the MNIST dataset using Keras API and plot them using the matplotlib library.</div><div style="font-family: georgia;"><br /></div><div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.utils import to_categorical</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.models import Sequential</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.layers import Conv2D</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.layers import MaxPooling2D</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.layers import Dense</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.layers import Flatten</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.optimizers import SGD</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from keras.datasets import mnist</span></div><div><span style="background-color: #eeeeee; font-family: courier;">from matplotlib import pyplot</span></div><div><span style="background-color: #eeeeee; font-family: courier;"><br /></span></div><div><span style="background-color: #eeeeee; font-family: courier;"># load dataset</span></div><div><span style="background-color: #eeeeee; font-family: courier;">(trainX, trainy), (testX, testy) = mnist.load_data()</span></div><div><span style="background-color: #eeeeee; font-family: courier;"># plot first 9 images</span></div><div><span style="background-color: #eeeeee; font-family: courier;">for i in range(9):</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> pyplot.subplot(330 + 1 + i)</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> pyplot.imshow(trainX[i], cmap=pyplot.get_cmap('gray'))</span></div><div><span style="background-color: #eeeeee; font-family: courier;">pyplot.show()</span></div><div style="font-family: georgia;"><br /></div><div style="font-family: georgia;">Training and testing images (that are already well defined by the model) are loaded separately as shown in the code above.</div><div><figure class="graf graf--figure" name="4af5"><span style="font-family: georgia;"><img alt="handwritten digits images in MNIST dataset" class="graf-image" data-height="311" data-image-id="1*Rzav8JaLce3b357LbqVWlQ.png" data-width="413" src="https://cdn-images-1.medium.com/max/1000/1*Rzav8JaLce3b357LbqVWlQ.png" /></span><figcaption class="imageCaption"><span style="font-family: georgia; font-size: x-small;">(Image by <a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" target="_blank">Author</a>) The first<strong class="markup--strong markup--figure-strong"> nine handwritten digits images in the MNIST dataset</strong></span></figcaption></figure></div></div><div>Now we will load the complete dataset and pre-process the data before feeding it to the neural network.</div><div><br /></div><div><span style="background-color: #eeeeee; font-family: courier;">(trainX, trainY), (testX, testY) = mnist.load_data()</span></div><div><span style="background-color: #eeeeee; font-family: courier;">trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))</span></div><div><span style="background-color: #eeeeee; font-family: courier;">testX = testX.reshape((testX.shape[0], 28, 28, 1))</span></div><div><span style="background-color: #eeeeee; font-family: courier;">trainY = to_categorical(trainY)</span></div><div><span style="background-color: #eeeeee; font-family: courier;">testY = to_categorical(testY)</span></div><div><br /></div><div>In the above, code we have reshaped the data to have a single color channel (since the images are of the same 28x28 pixel and greyscale form). </div><div><br /></div><div>Further, we have one hot encoded dataset values (using<span style="font-family: courier;"> <span style="background-color: #eeeeee;">to_categorical</span>,</span> a Keras function) because we know there are ten distinct classes that all are represented by unique integers. Here each integer sample is transformed into a ten element binary vector with a one for the index of the class value, and zero values for all other classes.</div><div><br /></div><div>After doing this, we will have to<b> normalize our dataset </b>as we know that the pixel value of images varies between 0 and 255 (black and white). For doing this we<b> scale this data into the range of [0,1].</b></div><div> </div><div><span style="background-color: #eeeeee; font-family: courier;">trainX = trainX.astype('float32')</span></div><div><span style="background-color: #eeeeee; font-family: courier;">testX = testX.astype('float32')</span></div><div><span style="background-color: #eeeeee; font-family: courier;">trainX = trainX / 255.0</span></div><div><span style="background-color: #eeeeee; font-family: courier;">testX = testX / 255.0</span></div><div><br /></div><div>In the above code, we have first converted the integral values of pixel to floats. After that, we have divided those values by the maximum number (i.e. 255) so that all the values will be scaled in the range of [0,1].</div><div><br /></div><div>Now we will start building our neural network.</div><div><br /></div><div><span style="background-color: #eeeeee; font-family: courier;"> model = Sequential()</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> model.add(Conv2D(32, (3, 3), activation='relu', </span></div><div><span style="background-color: #eeeeee; font-family: courier;"> kernel_initializer='he_uniform', input_shape=(28, 28, 1)))</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> model.add(MaxPooling2D((2, 2)))</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> model.add(Flatten())</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> model.add(Dense(100, activation='relu', </span></div><div><span style="background-color: #eeeeee; font-family: courier;"> kernel_initializer='he_uniform'))</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> model.add(Dense(10, activation='softmax'))</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> opt = SGD(lr=0.01, momentum=0.9)</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> model.compile(optimizer=opt, loss='categorical_crossentropy',metrics=['accuracy'])</span></div><div><br /></div><div>In the above code, we have used <span style="background-color: #eeeeee; font-family: courier;">Keras API sequentaial()</span><span style="background-color: white; font-family: courier;"> </span>that is used to create a model layer by layer. After that, we have added a single convolution layer for our model with a kernel size of (3x3) with 32 filters. It is followed by a single <span style="background-color: #eeeeee;"><span style="font-family: courier;">MaxPooling()</span></span> layer of the kernel size (2x2). Then the output feature map is flattened.</div><div><br /></div><div>As we know that there are 10 classes, so there will be <b>10 nodes required in the output layer</b> for the prediction of each class (multi-class classification) along with the <b>softmax activation function.</b> Between the feature extractor layers and output layer, we have added a dense layer with 100 nodes for feature analysis and interpretation by the model.</div><div><br /></div><div>Stochastic gradient descent(with a learning rate of 0.01 and momentum of 0.9) optimizer and categorical_crossentropy loss function is used in the model (suitable for multi-class classification models).<div class="separator" style="clear: both; text-align: center;"><br /></div><br /></div><div><br /></div><div>Finally, after compiling our model, it needs to be trained on the training dataset and tested on the testing dataset, and further to evaluate its results (i.e. accuracy and loss).</div><div><br /></div><div><span style="background-color: #eeeeee; font-family: courier;">batch_size = 128</span></div><div><span style="background-color: #eeeeee; font-family: courier;">num_epoch = 10</span></div><div><span style="background-color: #eeeeee; font-family: courier;">#model training</span></div><div><span style="background-color: #eeeeee; font-family: courier;">model_log = model.fit(trainX, trainY,</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> batch_size=batch_size,</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> epochs=num_epoch,</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> verbose=1,</span></div><div><span style="background-color: #eeeeee; font-family: courier;"> validation_data=(testX, testY))</span></div><div><span style="background-color: #eeeeee; font-family: courier;"><br /></span></div><div>In the above code, we have used 10 epochs with a batch_size of 128 (batch size is the number of samples trained in one iteration).The above results can be evaluated in terms of performance:</div><div><br /></div><div><span style="background-color: #eeeeee; font-family: courier;">score = model.evaluate(testX, testY, verbose=0)</span></div><div><span style="background-color: #eeeeee; font-family: courier;">print('Test loss:', score[0]) </span></div><div><span style="background-color: #eeeeee; font-family: courier;">print('Test accuracy:', score[1])</span></div><div><span style="background-color: #eeeeee; font-family: courier;"><br /></span></div><div><span style="background-color: white; font-family: georgia;"><p>With a test accuracy >98% we can say that our model is trained well for accurate prediction. You can also visualize these results using the matplotlib library!<br /></p><h2>Conclusion</h2>I hope with this article you will be able to understand and grasp the concepts of convolutional neural networks.<p></p><p>For a better understanding of these concepts, I will recommend you try writing these codes on your once. Keep exploring, and I am sure you will discover new features along the way. </p><p>If you have any questions or comments, please post them in the comment section.</p></span></div></div></div></div>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com12tag:blogger.com,1999:blog-8707052728183762633.post-14744920874639473692020-09-14T07:21:00.002-07:002020-09-14T08:13:31.166-07:00Introduction to Data Preprocessing<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Data preprocessing" class="graf-image" data-height="667" data-image-id="0*BB23rwB-Ib_ozAjq" data-width="1000" src="https://cdn-images-1.medium.com/max/1000/0*BB23rwB-Ib_ozAjq" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">Photo by Lucas Benjamin on Unsplash</span></td></tr></tbody></table><p style="text-align: justify;"><span style="font-family: georgia;"><br />Data is a collection of facts and figures, observations, or descriptions of things in an unorganized or organized form. Data can exist as images, words, numbers, characters, videos, audios, and etcetera.</span></p><h2 style="text-align: justify;"><span style="font-family: georgia;">What is data preprocessing</span></h2><p class="graf graf--p" name="80a5" style="text-align: justify;"><span style="font-family: georgia;">To analyze our data and extract the insights out of it, it is necessary to process the data before we start building up our machine learning model i.e. we need to convert our data in the form which our model can understand. Since the machines cannot understand data in the form of images, audios, etc.</span></p><p class="graf graf--p" name="80a5" style="text-align: center;"><span style="text-align: left;"><span style="font-family: georgia;"><i><b>Data is processed in the form (an efficient format) that it can be easily interpreted by the algorithm and produce the required output accurately.</b></i></span></span></p><p class="graf graf--p" name="cd8e" style="text-align: justify;"><span style="font-family: georgia;">The data we use in the real world is not perfect and it is incomplete, inconsistent (with outliers and noisy values), and in an unstructured form. Preprocessing the raw data helps to organize, scaling, clean (remove outliers), standardize i.e. simplifying it to feed the data to the machine learning algorithm.</span></p><h2 style="text-align: justify;"><strong class="markup--strong markup--p-strong"><span style="font-family: georgia;">The process of data preprocessing involves a few steps:</span></strong></h2><ul class="postList"><li class="graf graf--li" name="6da9" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Data cleaning:</u> </strong>the data we use may have some missing points (like rows or columns which does not contain any values) or have noisy data (irrelevant data that is difficult to interpret by the machine). To solve the above problems we can delete the empty rows and columns or fill them with some other values and we can use methods like regression and clustering for noisy data.</span></li><li class="graf graf--li" name="5024" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Data transformation:</u> </strong>this the process of transforming the raw data into the format that is ready to suitable for the model. It may include steps like- categorical encoding, scaling, normalization, standardization, etc.</span></li><li class="graf graf--li" name="aebb" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Data reduction:</u> </strong>this helps to reduce the size of the data we are working on (for easy analysis) while maintaining the integrity of the original data.</span></li></ul><h2 style="text-align: justify;"><span style="font-family: georgia;">Scikit-learn library for data preprocessing</span></h2><p class="graf graf--p" name="7f52" style="text-align: justify;"><span style="font-family: georgia;"><a class="markup--anchor markup--p-anchor" data-href="https://scikit-learn.org/" href="https://scikit-learn.org/" rel="noopener" target="_blank">Scikit-learn</a> is a popular machine learning library available as an open-source. This library provides us various essential tools including algorithms for random forests, classification, regression, and of course for <strong class="markup--strong markup--p-strong">data preprocessing as well. </strong>This library is built on the top of NumPy and SciPy and it is easy to learn and understand.</span></p><p class="graf graf--p" name="b059" style="text-align: justify;"><span style="font-family: georgia;">We can use the following code to import the library in the workspace:</span></p><pre class="graf graf--pre" name="56ff" style="text-align: justify;"><span style="background-color: #cccccc;">import sklearn</span></pre><div style="text-align: justify;"><span style="font-family: georgia;">For including the features for preprocessing we can use the following code:</span></div><pre class="graf graf--pre" name="875f" style="text-align: justify;"><span style="background-color: #cccccc;">from <strong class="markup--strong markup--pre-strong">sklearn</strong> import <strong class="markup--strong markup--pre-strong">preprocessing</strong></span></pre><div style="text-align: justify;"><div style="text-align: center;"><b><i><span style="font-family: georgia;">In this article, we will be focussing on some essential data preprocessing features like standardization, normalization, categorical encoding, discretization, imputation of missing values, generating polynomial features, and custom transformers.</span></i></b></div><div style="text-align: center;"><b><i><span style="font-family: georgia;"><br /></span></i></b></div><span style="font-family: georgia;">So, now let’s get started with these functions!</span></div><h2 style="text-align: justify;"><span style="font-family: georgia;">Standardization</span></h2><p class="graf graf--p" name="c849" style="text-align: justify;"><span style="font-family: georgia;">Standardization is a technique used to scale the data such that the mean of the data becomes zero and the standard deviation becomes one. Here the values are not restricted to a particular range. We can use standardization when features of input data set have large differences between their ranges.</span></p><figure class="graf graf--figure" name="4eb9"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Formula for standardization of data" class="graf-image" data-height="271" data-image-id="1*OUcXeN0RRE7n0lg6O8BRcw.png" data-width="526" src="https://cdn-images-1.medium.com/max/1000/1*OUcXeN0RRE7n0lg6O8BRcw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">The formula for standardization of data</span></td></tr></tbody></table></figure><p class="graf graf--p" name="8490" style="text-align: justify;"><span style="font-family: georgia;">Let us consider the following example:</span></p><pre class="graf graf--pre" name="f2e6"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">from sklearn import preprocessing</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">x = np.array([[1, 2, 3],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">[ 4, 5, 6],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">[ 7, 8, 9]])</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">y_scaled = <strong class="markup--strong markup--pre-strong">preprocessing.scale</strong>(X_train)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">print(y_scaled)</span></div></pre><p class="graf graf--p" name="f61c" style="text-align: justify;"><span style="font-family: georgia;">Here we have an input array of dimension 3x3 with its values ranging from one to nine. Using the </span><code class="markup--code markup--p-code" style="font-family: georgia;">scale</code><span style="font-family: georgia;">function available in the </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">preprocessing </span></code><span style="font-family: georgia;">we can quickly scale our data.</span></p><figure class="graf graf--figure" name="f9a6"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="data preprocessing standardization" class="graf-image" data-height="85" data-image-id="1*hyGdFujQmf4kgSwsc2Yh-w.png" data-width="401" src="https://cdn-images-1.medium.com/max/1000/1*hyGdFujQmf4kgSwsc2Yh-w.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"> Scaled data</span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div></figure><p class="graf graf--p" name="c78f" style="text-align: justify;"><span style="font-family: georgia;">There is another function available in this library </span><code class="markup--code markup--p-code" style="background-color: #cccccc;"><span style="font-family: courier;">StandardScaler</span></code><span style="font-family: georgia;">, this helps us to compute mean and standard deviation to the training set of data and reapplying the same transformation to the training dataset by implementing the </span><span style="background-color: #cccccc; font-family: courier;"><code class="markup--code markup--p-code">Transformer API</code> </span><span style="font-family: georgia;">.</span></p><p class="graf graf--p" name="0af3" style="text-align: justify;"><span style="font-family: georgia;">If we want to scale our features in a given range we can use the </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">MinMaxScaler</span></code><span style="font-family: georgia;">(using parameter </span><span style="background-color: #cccccc; font-family: courier;"><code class="markup--code markup--p-code">feature_range=(min,max)</code>)</span><span style="font-family: courier;"><span style="background-color: white;"> or </span><code class="markup--code markup--p-code" style="background-color: #cccccc;">MinAbsScaler </code></span><span style="font-family: georgia;">(the difference is that the maximum absolute value of each feature is scaled to unit size in </span><code class="markup--code markup--p-code" style="font-family: georgia;">MinAbsScaler</code><span style="font-family: georgia;">)</span></p><pre class="graf graf--pre" name="34b3"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">from sklearn.preprocessing import MinMaxScaler</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">x =<strong class="markup--strong markup--pre-strong"> MinMaxScaler(feature_range=(0,8))</strong></span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">y = np.array([[1, 2, 3],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">[ 4, -5, -6],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">[ 7, 8, 9]])</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">scale = x.<strong class="markup--strong markup--pre-strong">fit_transform</strong>(y)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">scale</span></div></pre><p class="graf graf--p" name="f336" style="text-align: justify;"><span style="font-family: georgia;">Here the values of an array of dimension 3x3 are scaled in a given range of </span><code class="markup--code markup--p-code" style="font-family: georgia;">(0,8)</code><span style="font-family: georgia;">and we have used the </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">.fit_transform()</span><span style="font-family: georgia;"> </span></code><span style="font-family: georgia;">function which will help us to apply the same transformation to another dataset later.</span></p><figure class="graf graf--figure" name="0167"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="standardization in data preprocessing" class="graf-image" data-height="72" data-image-id="1*cwI7Uln-QyZrgEeLWYUdpQ.png" data-width="466" src="https://cdn-images-1.medium.com/max/1000/1*cwI7Uln-QyZrgEeLWYUdpQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">Scaled data in a specified range</span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div></figure><h2 style="text-align: justify;"><span style="font-family: georgia;">Normalization</span></h2><div style="text-align: justify;"><span style="font-family: georgia;">Normalization is the process where the values are scaled in a range of <strong class="markup--strong markup--p-strong">-1,1 </strong>i.e. converting the values to a common scale. This ensures that the large values in the data set do not influence the learning process and have a similar impact on the model’s learning process. Normalization can be used when we want to quantify the similarity of any pair of samples such as dot-product.</span></div><pre class="graf graf--pre" name="fc26"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">from sklearn import preprocessing</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">X = [[1,2,3],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">[4,-5,-6],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">[7,8,9]]</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">y = preprocessing.normalize(X)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">y</span></div></pre><figure class="graf graf--figure" name="7ee6"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Normalized data in data preprocessing" class="graf-image" data-height="81" data-image-id="1*pQZ1Wj01cDHKrRcIQXPwgg.png" data-width="469" src="https://cdn-images-1.medium.com/max/1000/1*pQZ1Wj01cDHKrRcIQXPwgg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"> Normalized data</span></td></tr></tbody></table></figure><div style="text-align: justify;"><span style="font-family: georgia;">This module also provides us an alternative for </span><code class="markup--code markup--p-code" style="background-color: #cccccc;"><span style="font-family: courier;">Transformer API</span></code><span style="font-family: georgia;">, by using the </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">Normalizer</span></code><span style="font-family: georgia;"> function which implements the same operation.</span></div><h2 style="text-align: justify;"><span style="font-family: georgia;">Encoding categorical features</span></h2><p class="graf graf--p" name="8a0b" style="text-align: justify;"><span style="font-family: georgia;">Many times the data we use may not have the features values in a continuous form, but instead the forms of categories with text labels. To get this data processed by the machine learning model, it is necessary for converting these categorical features into a machine-understandable form.</span></p><p class="graf graf--p" name="5d99" style="text-align: justify;"><span style="font-family: georgia;">There are two functions available in this module through which we can encode our categorical features:</span></p><ul class="postList"><li class="graf graf--li" name="1fce" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>OrdinalEncoder: </u></strong>this is to convert categorical features to integer values such that the function converts each categorical feature to one new feature of integers (0 to n_categories — 1).</span></li></ul><pre class="graf graf--pre" name="909c"><div style="text-align: justify;"><span style="background-color: #cccccc;"><span style="font-family: courier;">import sklearn.preprocessing</span></span></div><div style="text-align: justify;"><span style="background-color: #cccccc;"><span style="font-family: courier;">import numpy as np</span></span></div><div style="text-align: justify;"><span style="background-color: #cccccc;"><span style="font-family: courier;">enc = <strong class="markup--strong markup--pre-strong">preprocessing.OrdinalEncoder</strong>()</span></span></div><div style="text-align: justify;"><span style="background-color: #cccccc;"><span style="font-family: courier;">X = [['a','b','c','d'], ['e', 'f', 'g', 'h'],['i','j','k','l']]</span></span></div><div style="text-align: justify;"><span style="background-color: #cccccc;"><span style="font-family: courier;">enc.fit(X)</span></span></div><div style="text-align: justify;"><span style="background-color: #cccccc;"><span style="font-family: courier;">enc.transform([['a', 'f', 'g','l']])</span></span></div></pre><div style="text-align: justify;"><span style="font-family: georgia;">Here, three categories are encoded as <code class="markup--code markup--p-code">0,1,2</code> and the output result for the above input is:</span></div><figure class="graf graf--figure" name="15a9"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="categorical encoding data preprocessing" class="graf-image" data-height="35" data-image-id="1*Zb0cv_TaR4ETp2kWzDXRRQ.png" data-width="268" src="https://cdn-images-1.medium.com/max/1000/1*Zb0cv_TaR4ETp2kWzDXRRQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"> Encoded data</span></td></tr></tbody></table></figure><ul class="postList"><li class="graf graf--li" name="283f" style="text-align: justify;"><strong class="markup--strong markup--li-strong" style="font-family: georgia;"><u>OneHotEncode:</u> </strong><span style="font-family: georgia;">this encoder function transforms each categorical feature with </span><code class="markup--code markup--li-code" style="font-family: georgia;">n_categories</code><span style="font-family: georgia;"> possible values into </span><code class="markup--code markup--li-code" style="background-color: #cccccc;"><span style="font-family: courier;">n_categories</span></code><span style="font-family: georgia;"> binary features, with one of them 1, and all others 0. Check the following example for a better understanding.</span></li></ul><pre class="graf graf--pre" name="e98b"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import sklearn.preprocessing</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">enc = <strong class="markup--strong markup--pre-strong">preprocessing.OneHotEncoder</strong>()</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">X = [['a','b','c','d'], ['e', 'f', 'g', 'h'],['i','j','k','l']]</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">enc.fit(X)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">enc.transform([['a', 'f', 'g','l']]).toarray().reshape(4,3)</span></div></pre><figure class="graf graf--figure" name="c983"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="categorical encoding data preprocessing" class="graf-image" data-height="100" data-image-id="1*EIVGDVhfnyPZIyw44LYnFg.png" data-width="216" src="https://cdn-images-1.medium.com/max/1000/1*EIVGDVhfnyPZIyw44LYnFg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><br /></td></tr></tbody></table></figure><h2 style="text-align: justify;"><span style="font-family: georgia;">Discretization</span></h2><p class="graf graf--p" name="9e8e" style="text-align: justify;"><span style="font-family: georgia;">The process of discretization helps us to separate the continuous features of data into discrete values (also known as binning or quantization). This is similar to creating a histogram using continuous data (where discretization focuses on assigning feature values to these bins). Discretization can help us introduce non-linearity in linear models in some cases.</span></p><pre class="graf graf--pre" name="d39e"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import sklearn.preprocessing </span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">X = np.array([[ 1,2,3],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;"> [-4,-5,6],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;"> [7,8,9]])</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">dis = preprocessing.KBinsDiscretizer(n_bins=[3, 2, 2], encode='ordinal')</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">dis.fit_transform(X)</span></div></pre><p class="graf graf--p" name="eb7a" style="text-align: justify;"><span style="font-family: georgia;">Using the </span><code class="markup--code markup--p-code" style="background-color: #cccccc;"><span style="font-family: courier;">KBinsDiscretizer()</span></code><span style="font-family: georgia;">, the function discretizes the features into </span><code class="markup--code markup--p-code"><span style="font-family: courier;">k</span></code><span style="font-family: georgia;"> bins. By default, the output is one-hot encoded, which we can change with the </span><code class="markup--code markup--p-code" style="font-family: georgia;">encode</code><span style="font-family: georgia;"> parameter.</span></p><figure class="graf graf--figure" name="eaa0"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="discretization data preprocessing" class="graf-image" data-height="77" data-image-id="1*vAhxv4OnZy5WQoAjo5kDMg.png" data-width="235" src="https://cdn-images-1.medium.com/max/1000/1*vAhxv4OnZy5WQoAjo5kDMg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">Data discretization</span></td></tr></tbody></table></figure><h2 style="text-align: justify;"><span style="font-family: georgia;">Imputation of missing values</span></h2><div style="text-align: justify;"><span style="font-family: georgia;">This process is used to process the missing values in the data (NaNs, blanks, etcetera) by assigning a value to them (imputing- based on the known part of the dataset) so that the data can be processed by the model. Let’s understand this with an example:</span></div><pre class="graf graf--pre" name="a37e"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">from sklearn.impute import SimpleImputer</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">impute =<strong class="markup--strong markup--pre-strong"> SimpleImputer(missing_values=np.nan, strategy='mean')</strong></span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">X = [[np.nan, 1,2], [3,4, np.nan], [5, np.nan, 6]]</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">impute.fit_transform(X)</span></div></pre><div style="text-align: justify;"><span style="font-family: georgia;">Here, we have used </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">SimpleImputer()</span></code><span style="font-family: georgia;"> function for imputing the missing values. The parameters used in this function are </span><code class="markup--code markup--p-code" style="background-color: #cccccc;"><span style="font-family: courier;">missing_values</span></code><span style="font-family: georgia;"> to specify the missing values to be imputed, </span><code class="markup--code markup--p-code" style="font-family: georgia;">strategy</code><span style="font-family: georgia;"> to specify how we want to impute the value, like in the above example we have used </span><code class="markup--code markup--p-code" style="font-family: georgia;">mean</code><span style="font-family: georgia;">, this means that the missing values will be replaced by the mean of column values. We can use other parameters for </span><code class="markup--code markup--p-code" style="font-family: georgia;">strategy</code><span style="font-family: georgia;">, like median, mode, </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">most_frequent</span></code><span style="font-family: georgia;"> (based on the frequency of occurrence of particular value in a column), or </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">constant</span></code><span style="font-family: georgia;"> (a constant value).</span></div><figure class="graf graf--figure" name="ec56"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Imputation of missing values data preprocessing" class="graf-image" data-height="79" data-image-id="1*fq3BGYTQVUpQLCbnE69GUQ.png" data-width="253" src="https://cdn-images-1.medium.com/max/1000/1*fq3BGYTQVUpQLCbnE69GUQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">Imputing missing values</span></td></tr></tbody></table><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><h2 style="text-align: justify;"><span style="font-family: georgia;">Generating polynomial features</span></h2><div style="text-align: justify;"><span style="font-family: georgia;">To get greater accuracy in the results of our machine learning model, sometimes it is good to introduce complexity in the model (by adding non-linearity). We can simply implement this by using the function </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">PolynomialFeatures()</span></code><span style="font-family: georgia;">.</span></div><pre class="graf graf--pre" name="6e6b"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">from sklearn.preprocessing import PolynomialFeatures</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">x = <strong class="markup--strong markup--pre-strong">np.array([[1,2],</strong></span></div><strong class="markup--strong markup--pre-strong"><div style="text-align: justify;"><strong class="markup--strong markup--pre-strong"><span style="background-color: #cccccc; font-family: courier;"> [3,4]])</span></strong></div></strong><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">nonl = <strong class="markup--strong markup--pre-strong">PolynomialFeatures(2)</strong></span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">nonl.fit_transform(x)</span></div></pre><figure class="graf graf--figure" name="899f"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Generating polynomial features" class="graf-image" data-height="61" data-image-id="1*6l-x0q813qmw4d33vxzZCg.png" data-width="390" src="https://cdn-images-1.medium.com/max/1000/1*6l-x0q813qmw4d33vxzZCg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">Generating polynomial features</span></td></tr></tbody></table></figure><div style="text-align: justify;"><span style="font-family: georgia;">In the example above, we have specified the degree of the non-linear model required to </span><code class="markup--code markup--p-code" style="font-family: georgia;">2</code><span style="font-family: georgia;"> in the </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">PolynomialFeatures()</span></code><span style="font-family: georgia;"> function. The feature values of the input array are transformed from<b><u> (X1, X2) to (1, X1, X2, X1², X1*X2, X2²)</u></b>.</span></div><h2 style="text-align: justify;"><span style="font-family: georgia;">Custom transformers</span></h2><div style="text-align: justify;"><span style="font-family: georgia;">If it is required to transform the entire data using a particular function (existing in python) for any purpose like data processing or cleaning, we can create a custom transformer by implementing the function </span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">FunctionTransformer()</span></code><span style="font-family: georgia;"> and passing the required function through it.</span></div><pre class="graf graf--pre" name="8c04"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import sklearn.preprocessing </span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">transformer = <strong class="markup--strong markup--pre-strong">preprocessing.FunctionTransformer</strong>(np.log1p, validate=True)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">X = np.array([[1,2,3],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;"> [4,5,6],</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;"> [7,8,9]])</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">transformer.transform(X)</span></div></pre><div style="text-align: justify;"><span style="font-family: georgia;">In this example, we have used the log function to transform our dataset values.</span></div><figure class="graf graf--figure" name="1f6c"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Implementing custom transformers" class="graf-image" data-height="73" data-image-id="1*jYOqF8FUDgZyKSxsQbDNTg.png" data-width="448" src="https://cdn-images-1.medium.com/max/1000/1*jYOqF8FUDgZyKSxsQbDNTg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"> Implementing custom transformers</span></td></tr></tbody></table></figure><h2 style="text-align: justify;"><span style="font-family: georgia;">Conclusion</span></h2><div style="text-align: justify;"><span style="font-family: georgia;">I hope with this article you would have understood the concepts and need of data preprocessing in machine learning models and will be able to apply these concepts in the real data sets.<br /></span><span style="font-family: georgia;">For a better understanding of these concepts, I will recommend you try implementing these concepts on your once. Keep exploring, and I am sure you will discover new features along the way.<br /></span><span style="font-family: georgia;">If you have any questions or comments, please post them in the comment section.</span></div><div style="text-align: justify;"><br /></div><div style="text-align: justify;"><span style="font-family: georgia;">Check out the complete data visualization guide and essential functions of NumPy:</span></div><div style="text-align: justify;"><span style="font-family: georgia;"><a href="https://patataeater.blogspot.com/2020/08/data-visualization-with-python.html" target="_blank">Data visualization with python</a></span></div><div style="text-align: justify;"><span style="font-family: georgia;"><a href="https://patataeater.blogspot.com/2020/09/numpy-cheatsheet-for-essential-functions-python.html" target="_blank">Cheatsheet for NumPy: Essential and Lesser-Known Functions</a></span></div><div style="text-align: justify;"><span style="font-family: georgia;"><br /></span></div><div style="text-align: justify;"><pre class="graf graf--pre" name="a23a"><span style="font-family: georgia; font-size: x-small;">Resources:<br /><a class="markup--anchor markup--pre-anchor" data-href="https://scikit-learn.org/stable/modules/preprocessing.html#" href="https://scikit-learn.org/stable/modules/preprocessing.html#" rel="nofollow noopener noopener" target="_blank">https://scikit-learn.org/stable/modules/preprocessing.html#</a></span></pre></div>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com25tag:blogger.com,1999:blog-8707052728183762633.post-67064590060744759222020-09-03T10:16:00.001-07:002020-09-03T10:16:10.361-07:00Cheatsheet for NumPy: Essential and Lesser-Known Functions<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Numpy python" class="graf-image" data-height="588" data-image-id="1*bx9qfZE5gb2GBmHJk9xnhA.png" data-width="870" src="https://cdn-images-1.medium.com/max/1000/1*bx9qfZE5gb2GBmHJk9xnhA.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: georgia; font-size: x-small;"><span style="text-align: left;">(Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@chrisliverani?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" href="https://unsplash.com/@chrisliverani?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" rel="noopener" style="text-align: left;" target="_blank">Chris Liverani</a><span style="text-align: left;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/s/photos/mathematics?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" href="https://unsplash.com/s/photos/mathematics?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" rel="noopener" style="text-align: left;" target="_blank">Unsplash</a><span style="text-align: left;">)</span></span></td></tr></tbody></table><p style="text-align: justify;"><span style="font-family: georgia;"><u>Numpy</u> (<strong class="markup--strong markup--p-strong">stands for — Numerical Python</strong>) is a library available in Python programming language, supporting matrix data structures and multidimensional array objects. This the most basic scientific computing library that we need to learn, to begin our journey in the field of data science.</span></p><p class="graf graf--p" name="9299" style="text-align: justify;"><span style="font-family: georgia;">Numpy can compute <strong class="markup--strong markup--p-strong">basic mathematical calculations</strong> to make the process of creating advanced machine learning and artificial intelligence applications easier (by using comprehensive mathematical functions available within the library). Numpy allows us to <strong class="markup--strong markup--p-strong">carry out various complex mathematical calculations effortlessly</strong> along with several top-up libraries (like matplotlib, pandas, scikit-learn, etc.) built over it.</span></p><p class="graf graf--p" name="1929" style="text-align: justify;"><span style="font-family: georgia;">This library is a great tool for every data science professional to <strong class="markup--strong markup--p-strong">handle and analyze the data efficiently</strong>. Moreover, it is much easier to perform mathematical operations with numpy arrays in comparison to python’s list.</span></p><p class="graf graf--p" name="1fef" style="text-align: justify;"><span style="font-family: georgia;">Numpy library has various functions available in it. In this article, we will learn some <strong class="markup--strong markup--p-strong">essential</strong> and<strong class="markup--strong markup--p-strong"> lesser-known functions of this library</strong> and how to implement them efficiently.</span></p><p class="graf graf--p" name="1fef" style="text-align: justify;"><span style="font-family: georgia;"><i><b>Note: In this article, we will be using <a href="https://colab.research.google.com/" target="_blank">Google Colaboratory</a> to execute our codes.</b></i></span></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Importing numpy</span></h2><p class="graf graf--p" name="af75" style="text-align: justify;"><span style="font-family: georgia;">Numpy can be simply imported in the notebook by using the following code:</span></p><pre class="graf graf--pre" name="fade" style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import <strong class="markup--strong markup--pre-strong">numpy</strong> as <strong class="markup--strong markup--pre-strong">np</strong></span></pre><p class="graf graf--p" name="842f" style="text-align: justify;"><span class="markup--strong markup--p-strong"><em class="markup--em markup--p-em"><span style="font-family: georgia;"><u>Here, numpy is written as np to save time while coding, and also it is a de facto in the data science community.</u></span></em></span></p><p class="graf graf--p" name="2845" style="text-align: justify;"><span style="font-family: georgia;">Now, let’s get started with numpy functions!</span></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Creation of n-dimensional array using numpy</span></h2><p class="graf graf--p" name="08f4" style="text-align: justify;"><span style="font-family: georgia;">An array is a data structure in the numpy library, which is just like a<strong class="markup--strong markup--p-strong"> list</strong> which can store values, but the differences are that we can specify the data type of elements of an array ( <code class="markup--code markup--p-code">dtype</code> function) and arrays are faster and take less memory to store data, allowing the code to be optimized even further.</span></p><p class="graf graf--p" name="6399" style="text-align: justify;"><span style="font-family: georgia;">To create a <strong class="markup--strong markup--p-strong">single-dimensional array</strong> we can use the following code:</span></p><pre class="graf graf--pre" name="9fb5"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">array = <strong class="markup--strong markup--pre-strong">np.array(</strong>[1,2,3,4,5]<strong class="markup--strong markup--pre-strong">)</strong></span></div></pre><p class="graf graf--p" name="16be" style="text-align: justify;"><span style="font-family: georgia;">The process for creating a <strong class="markup--strong markup--p-strong">multi-dimensional</strong> array<strong class="markup--strong markup--p-strong"> </strong>is similar, we just have to add more values in <code class="markup--code markup--p-code">[]</code>brackets:</span></p><pre class="graf graf--pre" name="8f59" style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">array = <strong class="markup--strong markup--pre-strong">np.array(</strong>[[1.1,2.2,3.0,4.6,5.0],[6.4,7.3,8.5,9.1,10.2]<strong class="markup--strong markup--pre-strong">)</strong></span></pre><h2 style="text-align: justify;"><span style="font-family: georgia;">numpy.linsapce() function</span></h2><p class="graf graf--p" name="7677" style="text-align: justify;"><span style="font-family: georgia;">This </span><code class="markup--code markup--p-code" style="background-color: #cccccc;"><span style="font-family: courier;">numpy.linspace()</span></code><span style="font-family: georgia;"> function is used to create an array of evenly spaced numbers in a given interval. We can also determine the number of samples we want to generate (however, it is an optional parameter default value is set to fifty samples). Another optional parameter we can add to this function is </span><code class="markup--code markup--p-code" style="font-family: georgia;">restep</code><span style="font-family: georgia;"> which if </span><code class="markup--code markup--p-code" style="font-family: georgia;">True</code><span style="font-family: georgia;"> will return the </span><code class="markup--code markup--p-code" style="font-family: georgia;">space</code><span style="font-family: georgia;"> i.e. spacing between the samples along with the list. The function is: </span><code class="markup--code markup--p-code" style="font-family: georgia;">numpy.linspace(start, stop)</code><span style="font-family: georgia;"> . Let’s apply this function in an example:</span></p><pre class="graf graf--pre" name="e524"><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import numpy as np</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">import matplotlib.pyplot as plt</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">x = <strong class="markup--strong markup--pre-strong">np.linspace(0,10,10,dtype = int, retstep=True)</strong></span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">print(x)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">x = np.linspace(0,10,100)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">y = np.sin(x)</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">plt.plot(x,y,color = 'orange')</span></div><div style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;">plt.show()</span></div></pre><p class="graf graf--p" name="3da4" style="text-align: justify;"><span style="font-family: georgia;">As we can see here, even to calculate mathematical functions we are using<span style="background-color: white;"> </span></span><code class="markup--code markup--p-code"><span style="background-color: #cccccc; font-family: courier;">numpy</span></code><span style="font-family: georgia;"> library. We used the </span><code class="markup--code markup--p-code" style="font-family: georgia;">linspace()</code><span style="font-family: georgia;"> function to generate equally spaced values and used that array to plot </span><code class="markup--code markup--p-code" style="font-family: georgia;">sine</code><span style="font-family: georgia;"> function plot.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="sine function graph| linspace() function numpy" class="graf-image" data-height="345" data-image-id="1*res8QL3FLACqzqrFm3mISw.png" data-width="681" src="https://cdn-images-1.medium.com/max/1000/1*res8QL3FLACqzqrFm3mISw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) sine function graph using linspace() function to generate values</span></td></tr></tbody></table><h2 style="font-family: georgia; text-align: justify;"><br />Function for random sampling</h2><p class="graf graf--p" name="d5c9" style="text-align: justify;"><span style="font-family: georgia;">Here, the</span><span style="font-family: courier;"> <code class="markup--code markup--p-code" style="background-color: #cccccc;">numpy.random</code> </span><span style="font-family: georgia;">function helps us calculate random values in various ways like generating random values in a given shape, generating an array by randomly selecting values from a given 1D array, or randomly permute a sequence of a given array or a range.</span></p><ul class="postList" style="font-family: georgia;"><li class="graf graf--li" name="b910" style="text-align: justify;"><strong class="markup--strong markup--li-strong"><u>numpy.random.rand():</u> </strong>With this function, we can create an array of uniformly distributed values over given input shape in a range [0,1) (i.e. ‘1’ is excluded). For example:</li></ul><pre class="graf graf--pre" name="e87b" style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;"><strong class="markup--strong markup--pre-strong">np.random.rand</strong>(3,4)</span></pre><p class="graf graf--p" name="ef25" style="font-family: georgia; text-align: justify;">As we can see in this example, an array of shape (3,4) is generated with all values lying in a range of [0,1).</p><figure class="graf graf--figure" name="5b81" style="font-family: georgia;"><div style="text-align: justify;"><img alt="Numpy random function" class="graf-image" data-height="76" data-image-id="1*6yGKU93QAP2zZJB3Ho13dQ.png" data-width="578" src="https://cdn-images-1.medium.com/max/1000/1*6yGKU93QAP2zZJB3Ho13dQ.png" /></div><div style="text-align: center;"><span style="font-size: x-small;"><span style="text-align: justify;">(Image by </span>Author<span style="text-align: justify;">) </span><span class="markup--strong markup--figure-strong" style="text-align: justify;">Generating random values</span></span></div></figure><ul class="postList"><li class="graf graf--li" name="fdfc" style="text-align: justify;"><strong class="markup--strong markup--li-strong" style="font-family: georgia;"><u>numpy.random.choice():</u> </strong><span style="font-family: georgia;">This random function returns an array of random samples from a given input array. Other optional parameters that we can define are- </span><code class="markup--code markup--li-code"><span style="background-color: #cccccc; font-family: courier;">size</span></code><span style="font-family: georgia;"> i.e. the output shape of the array, </span><code class="markup--code markup--li-code" style="font-family: georgia;">replace</code><span style="font-family: georgia;"> i.e. whether we want repeated values in our output array and </span><code class="markup--code markup--li-code"><span style="background-color: #cccccc; font-family: courier;">p</span></code><span style="font-family: georgia;"> i.e. probability for each given sample of the input array. Check out the following example:</span></li></ul><pre class="graf graf--pre" name="6d03" style="text-align: justify;"><span style="background-color: #cccccc; font-family: courier;"><strong class="markup--strong markup--pre-strong">np.random.choice</strong>([1,2,3,4],(1,2,3),replace=True,p=[0.2,0.1,0.4,0.3])</span></pre><p class="graf graf--p" name="c9e7" style="font-family: georgia; text-align: justify;">Here, we have given the following input parameters to the functions- an input array with four elements, shape of output array ( <code class="markup--code markup--p-code">1</code> in the above code is the numbers of the arrays we want as output and <code class="markup--code markup--p-code">2,3</code> is output shape), repetition of values is <code class="markup--code markup--p-code">True</code> and probability for each sample (where the sum of values should be equal to one).</p><figure class="graf graf--figure" name="9c46" style="font-family: georgia;"><div style="text-align: justify;"><img alt="Random sampling | numpy python" class="graf-image" data-height="58" data-image-id="1*sXgfx6FngcTaKm0T9JWvCw.png" data-width="329" src="https://cdn-images-1.medium.com/max/1000/1*sXgfx6FngcTaKm0T9JWvCw.png" /></div></figure><ul class="postList" style="font-family: georgia;"><li class="graf graf--li" name="8bad" style="text-align: justify;"><strong class="markup--strong markup--li-strong"><u>np.random.permutation():</u> </strong>This function returns an array with a randomly permutated sequence (in case of input array) or a permuted range (in case of single-input).</li></ul><pre class="graf graf--pre" name="ea1e"><div style="text-align: justify;"><span style="font-family: courier;"><span style="background-color: #cccccc;">arr = np.random.permutation(5)
print('Permutation of a range: ' + str(arr))
arr_ = np.random.permutation([1,2,3,4,5,6,7,8,9])
print('Permutation of elements of input array: ' + str(arr_))</span></span></div><div style="text-align: justify;"><span style="font-family: courier;"><span style="background-color: #cccccc;"><br /></span></span></div><div style="text-align: justify;"><p class="graf graf--p" name="f1c2" style="font-family: georgia; white-space: normal;">In the first case, we have returned a permuted array over an input range and in the second case, we have returned a permuted array over an input array.</p><p class="graf graf--p" name="f1c2" style="font-family: georgia; white-space: normal;"><img alt="Random permutation using numpy" class="graf-image" data-height="61" data-image-id="1*I38yjPypdQdHM69FKw7Ohw.png" data-width="601" src="https://cdn-images-1.medium.com/max/1000/1*I38yjPypdQdHM69FKw7Ohw.png" /></p><div><p class="graf graf--p" name="f1c2" style="font-family: "times new roman"; white-space: normal;"><span style="font-family: georgia;"><b><i>The functions available in the numpy.random are not only limited to these, but you can also find the complete exhaustive list of functions here: <a href="https://medium.com/r/?url=https%3A%2F%2Fdocs.scipy.org%2Fdoc%2Fnumpy-1.14.0%2Freference%2Froutines.random.html" target="_blank">numpy documentation page.</a></i></b></span></p><h2 style="font-family: "times new roman"; text-align: left; white-space: normal;">Indexing and slicing of an array</h2></div><div><span id="docs-internal-guid-050fe341-7fff-65de-fc8e-a416e9af1c2c"><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">To access and modify the objects of an array, we use indexing and slicing methods. Index values of the first element in the array of length </span><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">n</span></span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">, start from</span><span style="font-family: courier;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> </span><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">0</span></span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> value and index for the last element of the array will be </span><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">n-1</span></span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> .</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">a = [1,2,3,4,5,6]</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">b = a[3]</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">#output = 4</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">In the above example, this indexing method will return the fourth element of the array a.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">For basic slicing of the array (i.e. splitting the array, in simple words), we use </span><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">[start:stop:step_size]</span></span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> notation.</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">arr[1:7:2]</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">#output array([1, 3, 5])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-family: georgia;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><u>Advanced indexing and slicing: </u></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">For a multi-dimensional array, we can index and slice the array by giving input of specific rows and columns values( in </span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">[rows,column]</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> format). For better understanding check the following example:</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">x = np.array([[ 0, 1, 2],</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">[ 3, 4, 5],</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">[ 6, 7, 8]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: courier;">x[0:2,1:2]</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Here, we have chosen the index of the first two rows (i.e. </span><span style="background-color: #cccccc; font-family: courier;">0:2</span><span style="font-family: georgia;"> in code) and a single column with index 1 (i.e. </span><span style="background-color: #cccccc;"><span style="font-family: courier;">1:2 </span></span><span style="font-family: georgia;">in code).</span></span></p><div class="separator" style="clear: both; text-align: center;"><span style="border: none; display: inline-block; height: 64px; margin-left: 1em; margin-right: 1em; overflow: hidden; width: 147px;"><img alt="Advanced indexing and slicing numpy" height="64" src="https://lh4.googleusercontent.com/4AfLyYyVBTy53-oXwTeOA8x3uZhGNP8W-SAtRUufc_Ww6qTzHnyjdBySPK7ka-kY5ck7bG3eg3kTQ8QtUhr_9tFct-4ryVQ8YNSt3-oBtUPAdjgI8mOWvDxUlqcpkxc5qCl1VcPB" style="margin-left: 0px; margin-top: 0px;" width="147" /></span></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: georgia;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></span></p><h2 style="line-height: 1.38; margin-bottom: 2pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">numpy.ravel() and numpy.flatten() functions</span></span></h2><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">These functions return a 1D flattened form of the input array.</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">arr = np.array([[1,2], [3,4],[5,6]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">x = arr.flatten()</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">print(x)</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">y = arr.ravel()</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">print(y)</span></span></p><div class="separator" style="clear: both; text-align: center;"><span style="border: none; display: inline-block; height: 59px; margin-left: 1em; margin-right: 1em; overflow: hidden; width: 168px;"><img alt="The output of the above code | numpy" height="59" src="https://lh6.googleusercontent.com/wN7D1eFmGLbUKSlWLMSo38nSLAfaY6D29Pau0vOwtjPs-yHPba7xLT0Fac4Blg7mqo8n1IF25-q4MT8Mj1PPjMUtuv9HYgFps7u1vRivhsi5XUFf14MTSzyHwK6OErKT2Hb5hO2T" style="margin-left: 0px; margin-top: 0px;" width="168" /></span></div><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">You may observe that the output of both functions is the same! Now you might wonder </span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">what is the difference between the two functions</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: georgia;"> as their output result is the same. It’s simple in </span><span style="background-color: #cccccc;"><span style="font-family: courier;">numpy.flatten()</span></span><span style="font-family: georgia;"> a copy of the original array is created while in </span><span style="background-color: #cccccc;"><span style="font-family: courier;">numpy.ravel()</span></span><span style="font-family: georgia;"> the original array is changed. Moreover, </span><span style="background-color: #cccccc; font-family: courier;">numpy.ravel()</span><span style="font-family: georgia;"> function is faster than </span><span style="background-color: #cccccc; font-family: courier;">numpy.flatten()</span><span style="font-family: georgia;"> as it does not occupy any memory.</span></span></p><h2 style="font-family: georgia; line-height: 1.38; margin-bottom: 2pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">numpy.isclose() function</span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: georgia;">This function is used to check whether two arrays are equal elements wise within tolerance and returns a boolean array.</span><span style="background-color: #cccccc;"><span style="font-family: courier;"> .isclosefunction</span></span><span style="font-family: georgia;"> array can be used to </span><span style="background-color: #cccccc;"><span style="font-family: courier;">assert</span></span><span style="font-family: georgia;">(verify) your code.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">def inv(arr):</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;"> arr = np.array(arr)</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><span style="background-color: #cccccc;"><span style="font-family: courier;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"> inverse = </span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">np.linalg.inv</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">(arr)</span></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;"> return inverse</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><span style="background-color: #cccccc;"><span style="font-family: courier;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">assert</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"> np.all(</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">np.isclose</span></span></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="background-color: #cccccc;"><span style="font-family: courier;">(inv(np.array([[6, 1, 1], [4, -2, 5], [2, 8, 7]])).tolist(),np.array([[0.17647058823529413,-0.0032679738562091526, -0.02287581699346405],[0.05882352941176469, -0.130718954248366, 0.0849673202614379],[-0.1176470588235294, 0.1503267973856209, 0.0522875816993464]])</span></span><span style="background-color: #cccccc; font-family: courier;">))</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">print("Sample Tests passed", '\U0001F44D')</span></span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">In this above, example we are finding the inverse of a given matrix using another numpy function numpy.linalg.inv() . After that we are verifying are result using assertfunction and we have used numpy.isclose() function to check the output values if they are close to the true values. The assert function will only work if all the values are True otherwise it will give an assertion error.</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="border: none; display: inline-block; height: 177px; overflow: hidden; width: 602px;"><img alt="Implementation of .isclose() function | numpy" height="177" src="https://lh6.googleusercontent.com/Op-JDotc6_PSDhsoI7V41atf7oU34i156hHdwlUMpQl8dxnlLEURrtma_jVnY2V-j4oiYwp20ZgmlTf06iUp2xeWD2BxmssLHlGQ7fXw4pTemHZYFqRbVmuGCfATBvsnRU8aueDw" style="margin-left: 0px; margin-top: 0px;" width="602" /></span></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;"> </span></p><span style="font-family: georgia;"><br /></span><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">Stack arrays in numpy</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">There are two functions available in numpy for stacking different arrays.</span></p><ul style="font-family: georgia; margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">numpy.hstack(): </span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">this function stacks the arrays column-wise (i.e. horizontally), similar to the concatenation of arrays along the second axis (except 1D array, where it concatenates along the first axis). For this function, the input arrays should be of the same shape (except 1D arrays, which can be of any length).</span></p></li></ul><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">a = np.array([[1,2],[3,4],[5,6]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">b = np.array([[7,8],[9,10],[11,12]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-family: courier;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">np.hstack</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">((a,b))</span></span></p><div class="separator" style="clear: both; font-family: georgia; text-align: center;"><span style="border: none; display: inline-block; height: 74px; margin-left: 1em; margin-right: 1em; overflow: hidden; width: 272px;"><img alt="Horizontal stacking of arrays using .hstack() function" height="74" src="https://lh3.googleusercontent.com/BT92yptBwJ8d7EbSpJUV2sNEV5iAbwmg5KZlk_q4D7dx3AAydeiyicG6IcZ51iCoZLzMXV_fscJJzpvkZfWYqiC_u_3QcP1euqOZw0OVl5Ppo8zIobVPNN3KsRhJsFlmWEJuQ8nx" style="margin-left: 0px; margin-top: 0px;" width="272" /></span></div><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"></span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">numpy.vstack(): </span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">this function stacks the arrays row-wise (i.e. vertically), similar to the concatenation of arrays along the first axis after 1-D arrays of shape (N,) have been reshaped to (1, N). For this function, the input arrays should be of the same shape (1D arrays must have the same length).</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">a = np.array([[1,2],[3,4],[5,6]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">b = np.array([[7,8],[9,10],[11,12]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-family: courier;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;">np.vstack</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">((a,b))</span></span></p><div class="separator" style="clear: both; font-family: georgia; text-align: center;"><span style="border: none; display: inline-block; height: 135px; margin-left: 1em; margin-right: 1em; overflow: hidden; width: 185px;"><img alt="Vertical stacking of arrays using .vstack() function | Numpy" height="135" src="https://lh4.googleusercontent.com/InIh5wAY3VfR_MOfNedRLDjecHa81XfP3npWH1uwTn_HN3AvGYsxxe0jwLpdwSOEpoZUhfsNp32O2aDOvvcOK8nqIiecapg0J8sSjDuPf0XXxRLsCq3TewGeX7qcFclcF8azEYD0" style="margin-left: 0px; margin-top: 0px;" width="185" /></span></div><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"></span></p><h2 style="font-family: georgia; line-height: 1.38; margin-bottom: 2pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">Statistics functions of numpy</span></h2><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">Numpy library has some useful functions for finding insights and analyzing the data statistically. We can calculate mean, median, variance, standard deviation, compute histogram over a set of data, and much more.</span></p><ul style="font-family: georgia; margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><u>numpy.mean():</u> </span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">with this function, we can calculate the arithmetic mean of a given array where we can also specify the axis.</span></p></li></ul><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">arr = a = np.array([[1, 2], [3, 4]])</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">np.mean(a,axis=1)</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline;"><span style="font-family: courier;">#output:</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="font-family: courier;">array([1.5, 3.5])</span></span></p><ul style="margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;"><u>numpy.histogram(): </u></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">this function helps us compute the histogram over a set of data. Here, we have to input a flattened array of data over which we want to compute the histogram, we can also define the number of </span><span style="background-color: #cccccc; font-family: courier;">bins</span><span style="font-family: georgia;"> (i.e. number of equal-width bins in a given range (optional)), and range of upper limit and limit of the bins (optional).</span></span></p></li></ul><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-family: courier; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">arr = np.array([1,2,3,2,2,3,4,5])</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="background-color: #cccccc; font-family: courier; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">np.histogram(arr, bins= [1,2,3,4,5])</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><span style="border: none; display: inline-block; height: 37px; overflow: hidden; width: 455px;"><img alt="Computing histogram using numpy" height="37" src="https://lh3.googleusercontent.com/xsoDiA4NGbvC7EzI2O9aJYzKVFrrg8XUlEWvuy6oJqDXA-2MLqET7tZucp5z-wkTGEyuttGpAcf7Os20oDhwXrozbWGZ6jhVXzs4fmHK0nt8HLeZ5MMnvsLC3M7xBLMYpVPYQ7BH" style="margin-left: 0px; margin-top: 0px;" width="455" /></span></span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">You can also visualize this histogram values on a plot using the matplotlib library.</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><br /></span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 0pt; margin-right: 30pt; margin-top: 0pt;"><b><i><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">You can find other numpy statistics functions here:</span><a href="https://numpy.org/doc/stable/reference/routines.statistics.html" style="text-decoration-line: none;"><span style="color: #1155cc; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline;"> numpy documentation page</span></a><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">.</span></i></b></p><h2 style="font-family: georgia; line-height: 1.38; margin-bottom: 2pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">Conclusion</span></h2><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">I hope with this article you must have learned some essential and new functions of this library. I would recommend you to try implementing these functions on your own for a better understanding.</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">Implementing these skills in daily use will definitely benefit you as a data science professional!</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">If you have any questions or comments, please post them in the comment section.</span></p><p dir="ltr" style="font-family: georgia; line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"></span></p><p class="graf graf--p" name="7cd1"><span style="font-family: georgia;">If you want to learn how to visualize the data and find the insights from it visually, then check out our<a href="https://patataeater.blogspot.com/2020/08/data-visualization-with-python.html" target="_blank"> another article here.</a></span></p><p class="graf graf--p" name="7cd1"><span style="font-family: georgia;"><br /></span></p><span style="font-family: georgia; font-size: x-small;"><pre class="graf graf--pre" name="38ae">Resources:<br /><a class="markup--anchor markup--pre-anchor" data-href="https://numpy.org/" href="https://numpy.org/" rel="noopener" target="_blank">https://numpy.org/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://numpy.org/doc/stable/" href="https://numpy.org/doc/stable/" rel="noopener" target="_blank">https://numpy.org/doc/stable/</a></pre></span></span></span></div></span></div></div></pre>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com45tag:blogger.com,1999:blog-8707052728183762633.post-53259312854151117892020-08-28T10:24:00.021-07:002020-08-31T10:27:34.614-07:00Complete Data Visualization Guide: Python<p style="text-align: center;"><span style="font-family: georgia;"><b><i><br /></i></b></span></p><p style="text-align: center;"><span style="font-family: georgia;"><b><i>“A picture is worth a thousand words”</i></b></span></p><p style="text-align: center;"><span style="font-family: georgia;"><b><i>-Fred R. Barnard </i></b></span></p><p class="graf graf--p" name="2373" style="text-align: justify;"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="2373" style="text-align: justify;"><span style="font-family: georgia;">Data visualization is a visual (or graphic) representation of data to<strong class="markup--strong markup--p-strong"> find useful insights</strong> (i.e. trends and patterns) in the data and making the process of data analysis easier and simpler.</span></p><p class="graf graf--p" name="21fb" style="text-align: justify;"><span style="font-family: georgia;">Aim of the data visualization is to make a quick and clear understanding of data in the first glance and make it visually presentable to comprehend the information.</span></p><p class="graf graf--p" name="6571" style="text-align: justify;"><span style="font-family: georgia;">In Python, several comprehensive libraries are available for creating high quality, attractive, interactive, and informative statistical graphics (2D and 3D).<span></span></span></p><a name='more'></a><p></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Some popular data visualization libraries available in Python</span></h2><ul class="postList"><li class="graf graf--li" name="738b" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Matplotlib</u></strong> is one such popular visualization library available which allows us to create high-quality graphics with a range of graphs such as scatter plots, line charts, bar charts, histograms, and pie charts.</span></li><li class="graf graf--li" name="9ba7" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Seaborn</u></strong> is another of Python’s data visualization library <strong class="markup--strong markup--li-strong">built on top of Matplotlib, </strong>which have a high-level interface with attractive designs. Moreover, it reduces the lines of code required to produce the same result as in Matplotlib.</span></li><li class="graf graf--li" name="e973" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Pandas</u></strong> is another great library available in Python for data analysis (data manipulation, time-series analysis, integrating indexing of data, etc.). <strong class="markup--strong markup--li-strong">Pandas Visualization</strong> (built on top of Matplotlib) is a tool of Pandas library that allows us to create a <strong class="markup--strong markup--li-strong">visual representation of data frames</strong> (data aligned in tabular form of columns and rows) <strong class="markup--strong markup--li-strong">and series</strong> (one-dimensional labeled array capable of holding data of any type) much quicker and easier way.</span></li><li class="graf graf--li" name="be03" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Plotly</u> </strong>library is used for creating interactive and multidimensional plots making the process of data analysis easier by providing a better visualization for the data. </span></li></ul><p class="graf graf--p" name="b09c" style="text-align: justify;"><span style="font-family: georgia;">With this article, we will be able to visualize the data in different forms by learning how to plot data in different Python libraries and understand where to use which one appropriately.</span></p><p class="graf graf--p" name="b09c" style="text-align: justify;"><span style="font-family: georgia;"><i><b>Note: We can use <a href="https://colab.research.google.com/" target="_blank">Google Colaboratory</a> to avoid the process of installation of libraries. All the libraries can be used by simply importing them in the notebook.<span></span></b></i></span></p><!--more--><p></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Understanding the basics of Maplotlib</span></h2><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Matplotlib data visualization" class="graf-image" data-height="450" data-image-id="1*YRjApzldGi8hUDdw0tbpWw.png" data-width="662" src="https://cdn-images-1.medium.com/max/1000/1*YRjApzldGi8hUDdw0tbpWw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"><span style="text-align: left;">(Image by </span>Author<span style="text-align: left;">) </span><strong class="markup--strong markup--figure-strong" style="text-align: left;">Elements of Graph</strong></span></td></tr></tbody></table><figure class="graf graf--figure" name="b504"><figcaption class="imageCaption"><br /></figcaption></figure><ul class="postList"><li class="graf graf--li" name="3ac3" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Figure: </u></strong>The entire area where everything is being drawn. It can contain multiple plots with axes, legends, a range of axes, grid, plot-title, etc.</span></li><li class="graf graf--li" name="da42" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Axes:</u> </strong>The area under the figure where the plot is being constructed (or the area your plot appears in) is known as axes. There can be multiple axes in a single figure.</span></li><li class="graf graf--li" name="3a8a" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Axis:</u></strong> This is the number line present in the graph which represents the range of values for the plot (X-axis and Y-axis as mentioned in the above figure). There can be more than two axis in the graph in the case of a multi-dimensional graph.</span></li><li class="graf graf--li" name="2018" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong"><u>Plot title:</u> </strong>The title is positioned in the center above the axes, giving an overview of the plot.<span><!--more--></span></span></li></ul><h2 style="text-align: justify;"><span style="font-family: georgia;">Importing the dataset</span></h2><div style="text-align: justify;"><span style="font-family: georgia;">In this article, at various points, we will be using the <a class="markup--anchor markup--p-anchor" data-href="https://archive.ics.uci.edu/ml/datasets/iris" href="https://archive.ics.uci.edu/ml/datasets/iris" rel="noopener" target="_blank"><strong class="markup--strong markup--p-strong">Iris data set</strong></a><strong class="markup--strong markup--p-strong"> (</strong>as an example), which is free and is commonly used (since it is one of the best-known databases to be found in the pattern recognition literature).</span></div><p class="graf graf--p" name="88d4" style="text-align: justify;"><span style="font-family: georgia;">We can import this data set in two ways:</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">1. Using Scikit-learn library:</span></h3><div><span style="font-family: georgia;"><img alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA74AAACNCAYAAACQeKafAAAgAElEQVR4Ae3dO28c1xnG8XySqVIlpYFQSBsDLkOlMeyCkJFChD5ADNlgQwGEEYACXLiLljACFSmpwg6gRo0EsAjgYkt+lxOcyzvnPnNm7zv7L4gld+dy5j2/Wc4zZ2b3d13XKX6oAQYwgAEMYAADGMAABjCAAQzM1cDv5rphbBc7LQYwgAEMYAADGMAABjCAAQxoAwRfRrwZ8ccABjCAAQxgAAMYwAAGMDBrAwRfgM8aOGf4OMOHAQxgAAMYwAAGMIABDBB8Cb4EXwxgAAMYwAAGMIABDGAAA7M2QPAF+KyBc3aPs3sYwAAGMIABDGAAAxjAAMGX4EvwxQAGMIABDGAAAxjAAAYwMGsDBF+Azxo4Z/c4u4cBDGAAAxjAAAYwgAEMEHwJvgRfDGAAAxjAAAYwgAEMYAADszZA8AX4rIFzdo+zexjAAAYwgAEMYAADGMAAwZfgS/DFAAYwgAEMYAADGMAABjAwawMEX4DPGjhn9zi7hwEMYAADGMAABjCAAQwQfAm+BF8MYAADGMAABjCAAQxgAAOzNkDwBfisgXN2j7N7GMAABjCAAQxgAAMYwADBl+BL8MUABjCAAQxgAAMYwAAGMDBrAwRfgM8aOGf3OLuHAQxgAAMYwAAGMIABDBB8Cb4EXwxgAAMYwAAGMIABDGAAA7M2cMTB90v108sP6uPNB/X2C85gHNxZrGd36mG5VEv5+XSnLrb9ZjJxndfvbPse3lzkO7lb1v2rY7V1oe4+BfVfPqi7Z8e6LbtqNzU7uPeRbb9nsPz8vY+aUBMMYAADGJipAYLvnjr2uxcf1MeXP6qv9rT+6gHuFgKfCZgbCL5TltMy7byDbxAWX92r5QaC78WbB7Vc3qvrQzO7jfaM1uxa3S+XqnjSZBvtYZkchGAAAxjAAAYwgIG1DOwp+J6py9cLtVgs1O3zs7U2oBrgDhwGwTcIZo191RJmxcOUaWWe6HELJwCi5Tdu80bmGQ1xbX1B8A3rRPDdiM1d7ges6yT/1+I0fN/idzxgAAOnbWDnwffs+a1aLG7V5dNLdUvwZcR3wsHolDA7ZdrimyDBNztIJviG/ywIvsX9ZsL+zPyhJ37HAwYwgAEMYGDbBnYbfJ9eqcXrS3WmD46erBp8/b29+v7ejze/qJ8+S6B88bP6ePOz+u6zH9V7M83P6rsumO/Ft+6g/lv11t0j/NXXv5j7he0y9fTJMjs7rX29vF67jGRdev1ySXPfHntvsl+W+1umy9adtsX+bcLdu2tlA8lSLd9dq84FNn1vbXx/anr/YnyZplmW3I9bePTLsgf8+u9+vWb6+iWwoyE0aLO9JzhYVvZaeN/qUi0rl1APrTPdVr9tQZ2D4BtNX1nf8I7aUDMzKhv3iSzTrt/XxPyd9HXe38G2jI742vb192NHl0WnryX1X/XSZ7OPhvtBaZ/rVLxfflAf+3032L6G/WVTNYvNp7Uo95/0I4/T+ox6US8MYAADGMAABjZpYLfBNzxAXTn4BgDMwXMt+P6i3r/UB9M2sL5/+Yt6//WX7kBaDrKDMNuHTheQ+7871bmDdD2/FF8OyMMP1pLndKD107p1JAfsm7jU2RzMf3pQDzoEuXDz8El/iJELufp5V/Prd8mHG9WCVhD4ZN74MQhCfQh06+v/Dvqo65RtZ/nDrSRIhOHTTB+FL7u8oeXEbRxeZz/t0Laa1x7Ug/6AqL6Obtv7v+Pt7JcbOje/t9SsVsN8nbY+OnT5PrV19H9HbRkKvgUHpT7Ry7PP+wAerSPb5oHa6BNA0f4gJ6Vkv7Tz2v0p3r/1c+E+19qGjdYs6FPu8R3o5ykmmLZ/r241zXTYwwAGMIABDEwzMOPgK+HTBU8XZOOD6fi1Hk8UqN1BeXSgroucB2QJvumBeSnklp7r1994EBgFxCjA1EJUiKMyzVAYDA74s5HWgXBVD6w21OXhIQ97ujb15YTbZX9vmnZoW91rPvTa5Q4GzGq/ue1JTwwkNSsuO5mmr0MQeq2bWi27/qRI/qnOzkAW5Ms2NhZ8S3VyJ5fCfWcT+4jsU9G+0q9/lZqJtYF5++XLtDxKP/CIBQxgAAMYwAAG9mVgxsFXRoriEddS8PWjsw6iuyTZHIS737NpOrkM049SxcseRr2Jg/oo3Lnga0dOy8ElRRbNLwfrQ2HQTFM54B+Yr7gevayozXG9SvOUnku3Sf5umnagzXLJeDgSbZY90GZZd/7YWDPXnvBEQGk7Ss91XS3ESp0Lo8GF9UnbSyG39JxMv/ZjuM85i3Iiqb9VQIyu8LixmvXrrvRp/3rsee36sFxGRDGAAQxgAAMYwMBaBgi+0WXJ9eAbjkTJQWwadNO/ZbrS486Drws5/j5Od39iOgo5FAbNzlY54B+Yrxw6BgKZjO4m949Wl1N4E2iadqDN1eA7NE+hHbbv22sWt9vOl4bveBoJWKsH33T5us2l0edNBl/j39x/H97nW/hObjcS3N8Pn115Ids//LixmvV9XOnT/vXh9pTeE3iOmmEAAxjAAAYwgIHtGSD4rh18j2DE14W19LLdYhgYDXaVA/6B+Yrr0QGhcBmv7OyleUrPyfTpY9O0A22uBt+BNqdt8H9PqFm4fPN7fk9tedu2EXzjdW8q+NrQK1dkuDe3woivr5+dpg/LK4TfjdWsD7aVPu1f396bdloX/qbWGMAABjCAAQxgYNwAwbcQfO3IrQTakXt8g4PwKSO+8TrGO6qEOTqYNyFJPsk5vtS5NHqnlxfN3x+wjx3Ql18fCkXl9ehtLi+r9vzQOtL61NcZ1HqF4GuWm4xEm3W7+mf3Ppu6lrezvD227/TlzmZd2f23lX4b2pYwTPf9rOtQC8uV56vLCWoaLb/0fHzrQd9vDcFXT7vqlRJFDyvVTLapUqPR7Zf5eez7npqtddkWdWRfwgAGMIABDLQZIPimwbd0T2/hg3dKIbf0XBWiW2bp3uHqPMkBYnQwPxB85V5af++oO2jXX0OUXuosYagU7sz6CyHOBQi//Bhf1M5kG0qh3ExfWr/bxtp6wroNrbOfbij4lF6LalzYRvO1ToV7aUsBf6Bmtibxpzb3bS6esHD9mfWla+NQYC1sU6lPzPpdm9MrB8K2jf9e+ATn4HJmf0uBnk5OPkmtK6E5MVVqQ+5hjZq59Vmnpf6W9vJY6guewwUGMIABDGAAA/swsNvg677CaLFYqPznVl0+aUDggml/z194n6B8BZE5kJZLKeOD5TicutfCZbjv9c06Izg4t+tOD8rlw65kvePbYtsS3OMo7W84kNftiw7mowCTH9T7MGXv7dX3dUbzR+u04Ta8H9jfB5q/VvoO2XR94bLS4JpNWwtw/b2n7v7kJLhnyzFB1E7r11luv21feGlvcHKgX85AyHH1z08kaAfldfqaJlYkYFbqYANXUAPdvnRkWJbRtz2YPl2utL2fNqxD0rYp00amwuUk+512XxzxTaZLT1JVlx+uy/6+8ZqZdedGvLO8Ddl7yoT2My/1xAAGMIABDGAAA+sZ2G3wPbgDPXtgvc6o6+kBtCGOA/wpO97Emg2MBmtv9RMWU9p0WtNSs9Pq79N7X6Z/6XMMYAADGMDAmAGC7xqjSGPFnefrE0PcwZ3s2MebwrSa2ZHr+qgrIW56H1Kz6TWb5/sXdaBfMYABDGAAA6dqgOBL8J34wSrTQtyp7ljxdk+oWXTJevmNmRBXrktc83gaahbXY6hWvEatMIABDGAAAxiYowGCL8GX4Lv1Uenx4Bven1y999e1kxA3/Z8RNZteszn+w2ObcIABDGAAAxg4XQMnHnxPt+PZ6el7DGAAAxjAAAYwgAEMYOBUDBB8tz7ax850KjsT24l1DGAAAxjAAAYwgAEMHKYBgi/Bd+KlzocJmTcY+gUDGMAABjCAAQxgAAMYqBkg+BJ8Cb4YwAAGMIABDGAAAxjAAAZmbYDgC/BZA6+d8eF5zgZiAAMYwAAGMIABDGDgdAwQfAm+BF8MYAADGMAABjCAAQxgAAOzNkDwBfisgXMW73TO4tHX9DUGMIABDGAAAxjAQM3AkQffL9VPLz+ojzf+5/3XX84jyL26V8vlg7p7NozXfD/pcqmW7mfsO2BrEI7jed/fb78YrstxbM8Gt6HRy8nVhRNb83g/pB/pRwxgAAMYwAAG1jRwxMFXQtDP6rs1i3CQYWBqkHl2px6WS0Xw3WCY3LCrr77+RX282ZLXqV6ibbtW98ulenhzcXBvqBdvHtRyea+uo/Yebh8f5HsJtTs41zhhH8YABjCAAQzs3sDug++TS3W7WKhF/3OrLp+ssOGf/aje33xQsxnhTQ9OpwaZkwi+KzhJ67rHvwm+0/uP4Du9ZvwjpWYYwAAGMIABDGAgN7Db4GtCbxx0z7/XITh+rqmjXPCd7SWvBN/ZjdIQfPM3oLF9neA7vWZjNeV1aooBDGAAAxjAwCka2G3wLY22uRHg2+dn04LOSPD1IUMuiXb3Ab/8UX2VtMNO6+8T/vjiW98Ws55f1E+ffavemnuJ9e+d+u5FfXktkNJ7c7PLTAvB14YAfT9v4dLPhhFfP7+9Jzhbp66LWa+/Zzhf14W6+7RUy3fXqnPrlPuLw+WZdX26UxedvYy2NE1Lnew0SR/e2D6I5m/uJ9uP+oRJ3O/lS5D7fnb3kadXGIw7EzeBr+Ce9JUvfU5qb+ub3hPu+iq4Bzzro+A16SN5DKfVtU7NGgPJvlSaLl2O7bfYRewsfS30WPKfTp/WgX9u0b5S6DNexwgGMIABDGAAA3M3cGTBtz1EhKHGhxU3fx9sJVCFocdNIwHZBar3L3XYstPr3004/uJn9bEUwkYOLG2AiMPr9bvkYD0JvhJayyGi60No7R7fbJ0uOEXL08/pQNu3X4JT2FZ5TgeQoM0uMMv6pb1D0/j1THijqdW8uZ8CQ9LHnXPQ/63bkzjQNTHr+GD73tWozZndPh+SJ2xv3xfBPEmtTR0TL/q5sqnSvbw2OEYWkvXq/pS+NeuT4B15kXAceim0o2Av8+nWbx3Fy4vduNAbteNa3ZuTLkHNku2Jl8F01AMDGMAABjCAAQzM3cD+g+/TK3O/79XTidhcCKld6iyBJH3djOBJwKmFKPP8B2XmjcJOEpTD6ZoPrIMR06F5wiDjgs5QMJHR1yicyPJd0EhfGw8VfgTYzyvBNw0jcXiS4Ovn0/0bT7PSzlXrs+Z+KgRaXadkubWQap/3I85Nzlw/1JY5rQ4VP6EX6ffs0c2bhcLV+iUPq5W2Je0w8zW2YdRoxfa0mk5870m2h3VRPwxgAAMYwAAGMHD4BvYcfM/VlfmQqyt1PvVgsin4+oBSwmgvYw1He6XDbDgyI8XRepKRwZWCr4yKucuFa9stQeaN/lqj0iidtNU9DgSAaniQdQx9ZVK23Fp4ittTXudqASvquySg9q8191PQt2Hth+avTieXSw87kzZuJPhm/eHq3tKXcrlyY+iUdtceS31sw/CQ7ZqBcmgurSNuj12e3kfikyyxx3geXqMeGMAABjCAAQxg4NQM7DH4Suhd4YOtdBCJgkoONx2ZK3VsNPobhhu5zFVfEh2tZzPBV7fFHtD7exez0VwTZPzrowf1tUAkYad6L2dwufLAtH79JxR8+0viA1/Og1w+3+JM7O08+DoTcs9u/7hS8PUBs1+OMZWO/I/YrrVJfEaXLMuy8nVITe2jMynLCC/Bj/broB95PrilgbrEnqgH9cAABjCAAQzM0cCegu+Zunxtv9Jo8iXOcsAaBdIcZ0sgGQu+2xrxjSH5g/Yo/AYjeDKK5sNnvr1DlzqPj5rZ5dn1xEE4Xy7BV3+NllxC3+JM+nunwVcCZhIkp1xmLO2WS9SXSWAed1WyXRvxLZjuTxCNBd9gXtluwi/BVv5X8IgFDGAAAxjAAAa6Tu0h+G4g9Gq8Gwi+6X2d/YF+eDlttJ7Njfj26zI7ogsIYUgJgm/XSYBIQmm4E7sD/mI4jpYVhIRwfvn05bAN+vVsufMMvmkoTf+W/kqft3+3Xepc9Rb1Q61/5PlScMx92FCaeykH34K/sE3u6oPU1njw1W1Ol53+LdtVeWyym8ybmU1eD7eN3/lHiAEMYAADGMAABk7CwI6D74ZCr8YZBdL8wLYtkCRB1qB3H34kl7lG60mmX+ke38InzroD9dqIrw1dcqlpZfRr8GBfglFlXrPdhWlc4Invn3TTJaN/EgzlsRyKSqEt7ztZRvExPCkRvkk191PhHl83r1y+bNebOAjMhdO1OXPb6NYTfV1WuA2Nv9uRed+XNsw+qIdwlNP1nTcl/btU6cit3l67zDwom1o4W+HXF9np068WmmK74b51XY/CuiMXr+6V30Zb51roj+ZrrDXzTNw/qetJHDiwX7BfYAADGMDAMRrYbfB1n+C8MB9oZS919r9PvNc3Cjo5vvZA4sJs8N2qYbCJA/Ymgm9wMN/fk1j4YJ7SSJeEgD7gSBj29wL7+y99MBKYPqwE00cjvMnydLjNAvUegq8ExqCPPsrv8gndkYehfnKBVuZ3j3LpstTKPubTptO1O3NG3cmSvv03pQ9Xyz3H7QpCrHzwmemnOLjaAOj7Wo/Y2pCsv185XUe8TO0oCpTBSRB5rXhyozcarzduf3kfiL76Kmxfsu74O3/lPmC/vvT1bN3hsvmdoIYBDGAAAxjAAAZOwsBugy+oTgLVYQeNwogvLnGJAQxgAAMYwAAGMICBWRsg+AJ81sDzEE7wzWuSjv7yNzXCAAYwgAEMYAADGJiXAYIvwZfgi4ETMzCvN3H+KdOfGMAABjCAAQxgYNwAwZfQc2KhhxFf3hjH3xipETXCAAYwgAEMYAAD8zJA8CX4nljwndcOzBsy/YkBDGAAAxjAAAYwgIFxAwRfgi/BFwMYwAAGMIABDGAAAxjAwKwNEHwBPmvgnP0aP/tFjagRBjCAAQxgAAMYwMDcDRB8Cb4EXwxgAAMYwAAGMIABDGAAA7M2QPAF+KyBz/3MFdvH2VkMYAADGMAABjCAAQyMGyD4EnwJvhjAAAYwgAEMYAADGMAABmZtgOAL8FkD5+zX+NkvakSNMIABDGAAAxjAAAbmboDgS/Al+GIAAxjAAAYwgAEMYAADGJi1gR0H33N1tVioRfRzqy6fcIZl7mdY2D6MYwADGMAABjCAAQxgAAP7MrDj4Jt39Pn3OggTfvcFgPXmJqkJNcEABjCAAQxgAAMYwMC8DOw9+HZPLtXtYqGuns6rsOwo9CcGMIABDGAAAxjAAAYwgIHDMEDw5Vr+WV/LzxvNYbzR0A/0AwYwgAEMYAADGMDAPg3sOfieqcvXC7V4fanOCKAEUAxgAAMYwAAGMIABDGAAAxjYgoE9BF8XdvsPuLpS51vYsH2eTWDdnM3CAAYwgAEMYAADGMAABjBwOAb2EHyTjX96ZT7l+fb5GWc2OAGAAQxgAAMYwAAGMIABDGAAAxs3sP/g23XKfLIzlztvvHM5w5ScZOENBGMYwAAGMIABDGAAAxg4SQMEX+CfJHxOCnBSAAMYwAAGMIABDGAAA6djYO/B9+z5rbnUma8zOh10vMHQ1xjAAAYwgAEMYAADGMDALg3sNvi67+xd9B9sxSc677KzWRdvLhjAAAYwgAEMYAADGMDAKRrYbfDlsmIuK8YABjCAAQxgAAMYwAAGMICBHRsg+O644Kd4doVt5qwiBjCAAQxgAAMYwAAGMLBPAwRfgi9nmzCAAQxgAAMYwAAGMIABDMzaAMEX4LMGvs+zSqybs5oYwAAGMIABDGAAAxg4DAMEX4IvwRcDGMAABjCAAQxgAAMYwMCsDRB8AT5r4JxhO4wzbPQD/YABDGAAAxjAAAYwsE8DBF+CL8EXAxjAAAYwgAEMYAADGMDArA0QfAE+a+D7PKvEujmriQEMYAADGMAABjCAgcMwQPAl+BJ8MYABDGAAAxjAAAYwgAEMzNoAwRfgswbOGbbDOMNGP9APGMAABjCAAQxgAAP7NEDwJfgSfDGAAQxgAAMYwAAGMIABDMzawBEH3wt192mplsulun+1/tmT63d2WQ9vLo6+w822fLpTF+y8a/Sl96WNLZcP6u7Z+s4mn+V6dqce1jLut+Nw95Mb9evjo/r1h6S+f3+rfnv8Tb39e/I8rtdwTS0n74N4wxsGMIABDGBgFgYIvg4ywdcdEK8dtLZ3YL23QP/qnuC7tf3kG/X2f4/q8X9v1TeFfyo3/31Uj4+/qpvCawSY7e1r1JbaYgADGMAABjAwNwN7Db7n3y/UYrFQi9eX6owD242dSVkrIBJ883446uB72G/a48F2OBjP7Q2Z7Tlsr/QP/YMBDGAAAxg4XgP7C75Pr2zoJfjmQWvNkwAE3w3vkATfjRs1/zTMpcyFS5xT/2663/79zXbaka6Pv6kzBjCAAQxgAAMYmJ2BPQXfM3X5eqEW358rM+o7acTX37M4dO/lxZsHtVzeq+sumT6591UucbbLGrpf+Frdm3s97b3Ae7vns7AT2m2VdrnHZDu7tA7LpQrvZ07rIPWQx/T+0Gz6d9eFnaO1Zul0ut9ceHUj0NKO7DHbznRZG7g3dyz4Zm0M2i/b0XVqtZrZ/kzrP362MXFfu0c52La4ffE2xK8N7Sf5dobOwnab0d7KJc7hdPr38ZHhDZ/sCPotbQt/U2sMYAADGMAABjBwfAb2EnzPnt+qxeJKnXfdCsE3KHJw0J7iC8OgP/B2oagU0gYv8S3Nd63us9AVtG1HB842kBRCStK263dJADS1i8OvqeFgHez26dpGQUyCX1TXxpq5eX0fSXCKt0m3zWxrsl1xvzeuc2rfNDgL62H7JK53U80Ktega+iOuQcHgQPs758CcUOj7z4XmUq1H2lP2GNfCttd+oFXzKO4Pv6rH0gdgTe1Lpi+coCqYoU7UCQMYwAAGMICBmRnYffB9cqluFwt19dQebE0f8Q0O0gYO6CX4hoFkMDwNHdAPvTYRhLQrG7lc5ZODK+0aD4i6hpVwU1nmWLjKAk/jcspttQE2DMODfSd90LjOsW3JXq86K7ez60oBPHDr2pvWrFiLTWxTtf2dkuCb1to6zU8+DAdxZ6oP0Pk297U1QXbKJzZPDMpigkf+aWMAAxjAAAYwgAEMdJ3aefBNg276d39g3AJ04IDeHriXRpoqB+ODAcMFmbW+Vqay3pbtrExTCyfFAFVYRnG6wTrUtyFvS0vNasGxHKCK7Y22q2Wd9W2o2qs5M8+XL/sdb2un4ppVarFif0TbUmu/rt3ANkTLkDqPtMdstz6JMxJ+v/n3bxM/rdl9yNV/b3jjlr7gEQsYwAAGMIABDGCg2cBug6/5QKtbdfnEh4/jCL66vS6M9ff5TgjVWwIZBydf02LocoElG2lOL2cdCTY2DPmAGS8vHSEcqVmtTVLjJDwVtyur7cg6s+l93YpBT09fC4615+WybHOPuSx/rGbzCL66htalv+c8HU3W0xB8xQWP1f1ulX2VeZr/+VN39j0MYAADGDg1AzsMvufqarFQt8/Pon/MxxN8g52jD2zTw28aCuLgOG15zcFX2tsSJEeDrwtwSWCutaXfoaQN0YcsVcJe5eC1Lfhupp/6dq8TfPsatdSsUovR/gi2t1K3anDvt608ah3VQJY9qT3+JEQafgm+Df0mNecx+p9RdEmNqBEGMIABDGAAAyMGdhd8w68v0l9hVPqZ9OnOAyNx/ajThCA56YC+U8P3Ou7ooLY04igBsw9dMgKX16IcJCsBTCBVLo0dDb56/qzGLhglgbx2YNu0DmmnPGbr9H1jtj/5dOviukt1Nsuv1Sp5vqlm5VpIG9N71YvtlG1OH6vtl31oW8FX17q8XR33+PLPKXXK35jAAAYwgAEMYGCLBnYXfCsbcRQjvq/uo6/+0aHDhrA8TE4KJJWatC/DBix/P6ULXJ8e1DIIvnIfpx918yNx0XSmPfJaetmyC4wSrIOwKuHMfn2Um661Zi4U+rb5YJrVYWza1nWa7XS105dVh7Uq9clAcCw5sPUI6tdYs3RZ5u9PD+ph3XvLB9ovNpqD9cCJBPOhXmkt3fR5/078sCo+1Zl/hKV9k+dwgQEMYAADGMBAo4HjC74SIuQ+0PAxOOhOQ0QWokyBgvATLsf8HgSXPuj6+xajkNdY7HIbBoJey3KjetggbgPTnboI5rf18O3XQccEtKBmvn15XaJg5AKoXKatQ41d/oo1i7ZB2lg+qZBuRxpas9ej+2zjWktgz0OZH52WbYwek5pl60xeN3VtrJm0yazPnFywfRHVP+hX32fxtsnoetRuMR62z7VrePm5B7/cuM9L660te/L3+DZ+52+1Ji11Yxr+eWIAAxjAAAYwgIFZGth78OUgNQks7Giz3NFwXnD+97fqt8dHNfpdvm66X38oLIP9hf0FAxjAAAYwgAEMYKDBAMG3oUiEFgIHBrZjwIz6Pg59n6/7GiNGe/mHxns1BjCAAQxgAAMYWMMAwXeN4hGGthOGqOsp1dUF28df1U1hX7TBuPwaTk7JCduKdwxgAAMYwAAG1jNA8C0cbINqPVTUb0v1K94LLfdEB4/BB48dR1/YD7rKLmU2lzgPjQZvqc68J3A2GQMYwAAGMIABDMzOAMEX1LNDfRxhj9BGP2EAAxjAAAYwgAEMYGBXBgi+BF+CLwYwgAEMYAADGMAABjCAgVkbIPgCfNbAd3UGifVwthIDGMAABjCAAQxgAAOHa4DgS/Al+GIAAxjAAAYwgAEMYAADGJi1AYIvwGcNnLNuh3vWjb6hbzCAAQxgAAMYwAAGdmWA4EvwJfhiAAMYwAAGMIABDGAAAxiYtQGCL8BnDXxXZ5BYD2crMYABDGAAAxjAAAYwcLgGCL5Tgu/nn6mL//xVvfjPn9Wfpsy3kWl/r/7yr7+qi3/8vhhU//RP3a7665N2wmd/Nst68a/P1B/WaLtp0z//WGzvpPas0QbWc7hvPvQNfQ+adwUAAAYaSURBVIMBDGAAAxjAAAYwsCsDBN8poWqDwdeEwgnB0gbbeuA+xODbuXrVwvqukLMe3lAxgAEMYAADGMAABjBw2gZ2HHzP1dVioRaFn6un0zoiHk38o/rbfz5Xf/l82jL2iX9K8P3DPz5XL45s+/rautHjvz07nr7p2z7lpAjTMrKPAQxgAAMYwAAGMICBgzWwl+B7+/xs7YLo4NiPJJqRxbkGXx3qg209wp3JjkbXR6sJmpwUwAAGMIABDGAAAxjAAAa2aeBIg6+937UfRTSjinGw6keE+8uT7T2w/TwmQNrlvND3oSbT9aFaT5e8Vrv3dXSd6XLM/cK2Xfr+3NJy7WhvvG0hCBsq/TLi7ZOdJwjPcv+uW3c0ffKaqUslaNt2+fUOTSv1i2paWW64bfwu/ccjFjCAAQxgAAMYwAAGMLCOgSMLvi6opoEx/NvdN+sDoR8Jzi8ZDpfnp+sGLs81y63cm+vX6YNqvk4Ldmg5vkODYD4WFF2ojoJsP48NviZcBx/MVWtb1w2vtzSffq68br29bnmVuvntZWemFhjAAAYwgAEMYAADGMDA5g3sJfim9/hOvb+36+J7ek0QS0KVDaFBmDUhMBj5NH9L8PVB1SJLp/OFHwqsxXVWAunQcnrolXn71/tg60ely+HTBd+kRjISm88zHHyb2h62reuUDctpnX1di9uULINpqBcGMIABDGAAAxjAAAYwsIqBHQffvJPOnt+aD7uadN+vCYQ+1JoglnxtTjmcpYFu+khkebl2u4qvVcJrcdo06JmRZ7+dgx1cWY+dxwXfpEb15aV1ivvNhtjypdnVZU7ZlrQO/L32PfHVfqG21BYDGMAABjCAAQxg4AQM7D34dt2Zuny9UIvXl+qsteAm5PnRQxMik1BXDpZpoCP4lgNRWqc4+Jp5JtwP7KdvDPGtDpiON2kMYAADGMAABjCAAQxgoMHAAQTfTp1/r7/i6EqdjzU4DVvhvb3JhzURfMOwutkR3zQsm1rr+icnH6LpGPHlDWls/+Z1jGAAAxjAAAYwgAEMbMnAAQRf992+3583d3J8T68Ndel9qsXgm10OvL8R36Z7XrP2hmE2+X1w2u0GXx1wi/UO0DZtbzB9FJp5vnnfoG7JfoEd7GAAAxjAAAYwgAEMdJ3ac/B1lzm3jPYGYE2I6kcXdajLL6HNg1gp5JaeGz5wzpfrpy++VgukbvR6+Ct+Gi45lrrU1mNe32Tw1W3yl5nboDW2/Ol1JsB5V9SCWmAAAxjAAAYwgAEMYGA9A7sNvk8u1e1CX9Yc/EwY6ZXONgFTgq8JfJXgm14KLfNIWGz8mh07Whl8Z22w3DC4Tgq+/ScdB8tNP3W5nyYNmtLpLnAG7bFfWaSXGc4zFkzt8kz7i8v6a/JVRfl6wzpIP/WPg6FctoXHvl69T2pCTTCAAQxgAAMYwAAGMLAJA7sNvjs8oC+G0B2ufxOdY5fRFlo3t77N71jz6YvN1+aQ+4220d8YwAAGMIABDGAAA3MxQPA9gjAsI87pfcxHgdBd0n2UbT8CG0dhgDpyXw0GMIABDGAAAxjAwJ4NEHz33AGtwcVehpxf0t06/16mc5c4D14GfST130v9qA3/IDCAAQxgAAMYwAAGMLARAwTfo4FkPyDqmEKkCevZfdVcLkKIxgAGMIABDGAAAxjAAAZ2a2C2wRdIu4VEvak3BjCAAQxgAAMYwAAGMHCoBgi+RzPiy050qDsR7cImBjCAAQxgAAMYwAAGDtsAwZfgu5Fr5tnRD3tHp3/oHwxgAAMYwAAGMICBUzZA8CX4EnwxgAEMYAADGMAABjCAAQzM2gDBF+CzBn7KZ7XYds7qYgADGMAABjCAAQxgwBog+BJ8Cb4YwAAGMIABDGAAAxjAAAZmbYDgC/BZA+cMF2c5MYABDGAAAxjAAAYwgAGCL8GX4IsBDGAAAxjAAAYwgAEMYGDWBgi+AJ81cM7ucXYPAxjAAAYwgAEMYAADGCD4EnwJvhjAAAYwgAEMYAADGMAABmZtgOAL8FkD5+weZ/cwgAEMYAADGMAABjCAAYIvwZfgiwEMYAADGMAABjCAAQxgYNYG/g/u7EOVojJBfgAAAABJRU5ErkJggg==" /></span></div><h3 style="text-align: justify;"><span style="font-weight: normal;"><span style="font-family: georgia; font-size: medium;"><span>Without downloading the </span><code class="markup--code markup--p-code"><span>.csv</span></code><span> file we can directly import the data set in the workspace using sci-kit learn library available in python.</span></span></span></h3><div><span><div style="text-align: justify;"><p class="graf graf--p graf--empty"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Importing Scikit learn iris dataset" class="graf-image" data-height="128" data-image-id="1*HwAkIHJXwXM2xEl0xcWMLQ.png" data-width="381" src="https://cdn-images-1.medium.com/max/1000/1*HwAkIHJXwXM2xEl0xcWMLQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: georgia; font-size: x-small;">(Image by Author) <b>First five heads in the data set</b></span></td></tr></tbody></table><h3><span style="font-size: medium;"><span style="font-family: georgia;"><br /></span></span></h3><h3><span><span style="font-family: georgia; font-size: large;">2. Using Pandas library:</span></span></h3><div><span><span><div><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/9a2faf2e3cbb365a94e28fc57358499d.js"></script>Using the above (by importing Pandas library) code, and downloading the </span><code class="markup--code markup--p-code"><span style="font-family: georgia;">.</span><b><span style="font-family: courier;">csv</span></b></code><span style="font-family: georgia;"> format of the dataset we can import the data in our workspace. These, are the first five elements in the </span><code class="markup--code markup--p-code" style="font-family: georgia;">iris</code><span style="font-family: georgia;"> dataset:</span></div><div><span><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="font-family: georgia; margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Importing iris dataset" class="graf-image" data-height="154" data-image-id="1*N3l4A9mVDYw0vq48j49gxA.png" data-width="774" src="https://cdn-images-1.medium.com/max/1000/1*N3l4A9mVDYw0vq48j49gxA.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author)<b> First five heads in the data set</b></span></td></tr></tbody></table><div style="font-family: georgia; text-align: center;"><b>Both of the above-mentioned methods can be used to import the dataset and to create graphs, but we will be using the latter because of the better readability of the data (as you can see the difference in the output results of both the methods).</b></div><h2 style="font-family: georgia;">Getting started with Matplotlib</h2><div style="font-family: georgia;">We begin by importing the library in our notebook by using the following code:</div><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/63681483532631750137867689612193.js"></script></span><div><div style="font-family: georgia;">There are various styles available in this library for drawing the plot.</div><h3 style="font-family: georgia;"><strong class="markup--strong markup--h4-strong">Line plots</strong></h3><p class="graf graf--p" name="e422" style="font-family: georgia;">Line plot or line chart represents the data in a series (in continuation) showing the frequency of data along with the number line. It can be used to compare numerical sets of values. This is one of the most simple graphs that we can make using python.</p><div><span style="font-family: georgia;">Here, using the </span><b><span style="font-family: courier;"><code class="markup--code markup--p-code"><span>numpy</span></code><span> <code class="markup--code markup--p-code">linspace()</code></span></span></b><span style="font-family: georgia;"> function we will generate data-points and store them in variable </span><code class="markup--code markup--p-code" style="font-family: georgia;">x</code><span style="font-family: georgia;"> and calculate the square of values of </span><code class="markup--code markup--p-code" style="font-family: georgia;"><b>x</b></code><span style="font-family: georgia;"> and store them in another variable </span><code class="markup--code markup--p-code" style="font-family: georgia;">y</code><span style="font-family: georgia;"> . </span></div><p class="graf graf--p" name="e7f4" style="font-family: georgia;">We will use <code class="markup--code markup--p-code"><b>plt.plot()</b></code> function to plot the graph and <code class="markup--code markup--p-code"><b>plt.show()</b></code> to display the graph.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="font-family: georgia; margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Line chart using matplotlib" class="graf-image" data-height="323" data-image-id="1*8AOR9bLeLMxPGsJi-hEtzg.png" data-width="603" src="https://cdn-images-1.medium.com/max/1000/1*8AOR9bLeLMxPGsJi-hEtzg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Line chart of y=x²</span></td></tr></tbody></table><p class="graf graf--p" name="509a" style="font-family: georgia;"><br />We can add some more functions to our plot to make it much easier to interpret.</p><ul class="postList"><li class="graf graf--li" name="b0fa"><strong class="markup--strong markup--li-strong"><span style="font-family: georgia;">To add a label</span></strong><strong class="markup--strong markup--li-strong" style="font-family: georgia;">:</strong><b><span style="font-family: courier;"><code class="markup--code markup--li-code">x-axis label</code> and </span></b><code class="markup--code markup--li-code"><b><span style="font-family: courier;">y-axis</span></b><span style="font-family: georgia;"> label</span></code><span style="font-family: georgia;"> we will use</span><span style="font-family: courier;"><b> </b><b><code class="markup--code markup--li-code">plt.xlabel()</code> and <code class="markup--code markup--li-code">plt.ylabel()</code></b></span><span style="font-family: georgia;"> functions respectively.</span></li><li class="graf graf--li" name="7507" style="font-family: georgia;">We can also give a <strong class="markup--strong markup--li-strong">title to our plot</strong> using the <b><code class="markup--code markup--li-code">plt.title()</code> </b>function.</li><li class="graf graf--li" name="3e2b" style="font-family: georgia;"><strong class="markup--strong markup--li-strong">A grid</strong> in the plot can simply be applied by calling <code class="markup--code markup--li-code"><b>plt.grid(True)</b></code> function (makes data easier to interpret).</li></ul><div><p class="graf graf--p" name="9553"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/266ed70e978112688e498e4529991824.js"></script>With the addition of these functions, the graph becomes much more readable and easier to analyze.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Line chart using matplotlib" class="graf-image" data-height="362" data-image-id="1*eqQ5q7CTtmtAozwNO4zX_A.png" data-width="596" src="https://cdn-images-1.medium.com/max/1000/1*eqQ5q7CTtmtAozwNO4zX_A.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Line chart of y=x²</span></td></tr></tbody></table><p class="graf graf--p" name="c2a8"><span style="font-family: georgia;"><br />We can add more than one line to our plot and make them distinguishable by using different colors and some other features:</span></p><p class="graf graf--p" name="ed17"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/1fea2a0b0e7db3df556ea319956f83e4.js"></script>In the above code, we have added another variable </span><code class="markup--code markup--p-code"><span style="font-family: courier;">z=x**3</span></code><span style="font-family: georgia;"> (z=x³) and changed the style and color of the line.<span></span></span></p><!--more--><p></p><p class="graf graf--p" name="ec8f"><span style="font-family: georgia;">To change the color of a line in the line plot we have to add </span><code class="markup--code markup--p-code"><span style="font-family: courier;">color=''</span></code><span style="font-family: georgia;"> parameter in </span><span style="font-family: courier;"><code class="markup--code markup--p-code">plt.plot()</code> </span><span style="font-family: georgia;">function.</span></p><p class="graf graf--p" name="082e"><span style="font-family: georgia;">To change the style of a line in the line plot we have to add </span><code class="markup--code markup--p-code"><span style="font-family: courier;">linestyle=’’</span></code><span style="font-family: georgia;"> parameter in </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.plot()</span></code><span style="font-family: georgia;"> function (or simply we can add ‘*’ or ‘- -’, etcetera).</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Line chart using matplotlib" class="graf-image" data-height="355" data-image-id="1*ZtaiM6YtL9dCdZq4NoYHeg.png" data-width="581" src="https://cdn-images-1.medium.com/max/1000/1*ZtaiM6YtL9dCdZq4NoYHeg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"><span style="font-family: georgia; text-align: justify;">(Image by </span>Author<span style="font-family: georgia; text-align: justify;">) </span><strong class="markup--strong markup--figure-strong" style="font-family: georgia; text-align: justify;">Line chart of y=x² and z=x³</strong></span></td></tr></tbody></table><p class="graf graf--p" name="392a"><span style="font-family: georgia;"><br />This makes the extraction of information and comparison of data variables easier.</span></p><p class="graf graf--p" name="c2a8"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="811b"><span style="font-family: georgia;">Similarly, we can create <strong class="markup--strong markup--p-strong">plots for mathematical functions</strong> as well:</span></p><p class="graf graf--p" name="3374"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/338ffed3c9a9a972bf96a93df94fd497.js"></script>Here, we have created a plot for </span><span style="font-family: courier;"><code class="markup--code markup--p-code">sin(x)</code> and <code class="markup--code markup--p-code">cos(x)</code> . </span></p><p class="graf graf--p" name="68fb" style="text-align: left;"><span style="font-family: georgia;">We can </span><strong class="markup--strong markup--p-strong" style="font-family: georgia;">adjust the limit of axes</strong><span style="font-family: georgia;"> by using the functions </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.xlim(lower_limit,upper_limit)</span></code><span style="font-family: georgia;"> for x-axis and</span><span style="font-family: courier;"> <code class="markup--code markup--p-code"><span style="font-family: courier;">plt.lim(lower_limit,upper_limit)</span></code></span><span style="font-family: georgia;"> for y-axis. </span></p><p class="graf graf--p" name="697e" style="text-align: left;"><span style="font-family: georgia;">For further labeling of the plot, we can add</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">legend</code></span><span style="font-family: georgia;"> with </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.legend()</span></code><span style="font-family: georgia;"> function, it will help to identify which line stands for which function.</span></p><figure class="graf graf--figure" name="7db6"><span style="font-family: georgia;"><img alt="Line chart using matplotlib" class="graf-image" data-height="338" data-image-id="1*wnBt9mGvk_7CFV8pCrHh9g.png" data-width="567" src="https://cdn-images-1.medium.com/max/1000/1*wnBt9mGvk_7CFV8pCrHh9g.png" /></span><figcaption class="imageCaption"><span style="font-family: georgia;">(Image by <a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" target="_blank">Author</a>) <strong class="markup--strong markup--figure-strong">Line chart of sin(x) and cos(x)</strong></span></figcaption></figure><h3><span style="font-family: georgia;">Subplots</span></h3><p class="graf graf--p" name="811b"><span style="font-family: georgia;"></span></p><div><span><span style="font-family: georgia;">For creating separate (multiple) plots in the same figure we can use the</span><span style="font-family: courier;"> </span></span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.subplots(num_rows,num_cols)</span></code><span style="font-family: georgia;"> function. Here the details of each subplot can be different.<br /></span><code class="markup--code markup--p-code"><span style="font-family: courier;"><script src="https://gist.github.com/kk189/b0ebf957ac9edad75f437c8a3476697c.js"></script>plt.sublots()</span></code><span style="font-family: georgia;"> function creates a figure and grid of subplots, in which we can define the number of columns and rows by passing an </span><code class="markup--code markup--p-code"><span style="font-family: courier;">int</span></code><span style="font-family: georgia;"> value as the parameter. Moreover, we can also change the spacing between the sublopts by using the</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">gridspec_kw={'hspace': , 'wspace': }</code></span><span style="font-family: georgia;"> argument. After that, by simply using the index number for the subplot we can easily plot the graphs.</span></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="subplots in matplotlib" class="graf-image" data-height="329" data-image-id="1*zMYFapD7_ov36McwZ4r3HQ.png" data-width="531" src="https://cdn-images-1.medium.com/max/1000/1*zMYFapD7_ov36McwZ4r3HQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: georgia; font-size: x-small;">(Image by Author) Four subplots in a single figure</span></td></tr></tbody></table><figure class="graf graf--figure" name="b026"><figcaption class="imageCaption"><br /></figcaption></figure><h3><strong class="markup--strong markup--h4-strong"><span style="font-family: georgia;">Scatter plots</span></strong></h3><p class="graf graf--p" name="b638"><span style="font-family: georgia;">This kind of plot uses ‘dots’ to represent the numerical data for different variables. </span></p><p class="graf graf--p" name="9495"><span style="font-family: georgia;">Scatter plots can be used to analyze how one variable affects the other variables. (We can use any number of variables we want to plot on the graph.)</span></p><p class="graf graf--p" name="0400"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="8eab"><span style="font-family: georgia;">We will use </span><code class="markup--code markup--p-code"><span style="font-family: courier;">dataset_name.plot()</span></code><span style="font-family: georgia;"> function to create the graph and in parameters, we will apply the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">kind = 'scatter’</span></code><span style="font-family: georgia;"> with a label for</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">x-axis</code> and <code class="markup--code markup--p-code">y-axis</code> </span><span style="font-family: georgia;">. Check out the example mentioned below (iris dataset).</span></p><p class="graf graf--p" name="7d66"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/de707d550502aa0a89f4e83902102cba.js"></script>Here, we are comparing the </span><span style="font-family: courier;"><code class="markup--code markup--p-code">petal length</code> and <code class="markup--code markup--p-code">petal width</code></span><span style="font-family: georgia;"> of different species of flowers present in the dataset.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Scatter plot for iris dataset" class="graf-image" data-height="340" data-image-id="1*zXOfMiuQb7LMKeXvyrmecQ.png" data-width="559" src="https://cdn-images-1.medium.com/max/1000/1*zXOfMiuQb7LMKeXvyrmecQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Iris dataset scatter plot</span></td></tr></tbody></table><p class="graf graf--p" name="804b"><span style="font-family: georgia;"><br />But, here it would be very difficult for us to analyze and extract information from this plot because we cannot differentiate between classes present. </span></p><p class="graf graf--p" name="4fc8"><span style="font-family: georgia;">So now, we will try another approach which will solve our problem. In this method, we will use </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.scatter()</span></code><span style="font-family: georgia;"> to create a scatter plot. <span></span></span></p><!--more--><p></p><p class="graf graf--p" name="8eab"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="1c2b"><strong class="markup--strong markup--p-strong" style="font-family: georgia;">To change the color</strong> <strong class="markup--strong markup--p-strong" style="font-family: georgia;">of dots</strong><span style="font-family: georgia;"> based on the species of flower, we can create a dictionary with storing the colors corresponding to the names of the species. By using the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">for</span></code><span style="font-family: georgia;"> loop we create a single scatter plot of three different species (each represented by a different color). </span></p><p class="graf graf--p" name="3797"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/41dd12f6f014f1370e0ab8a66910c62b.js"></script>This plot created is way better than the previous one. The data of species became easier to distinguish and gives an overall clarity for an easier analysis of information.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Scatter plot for iris dataset" class="graf-image" data-height="364" data-image-id="1*mOyTxd6yXMOcm5ZrlsH0Jw.png" data-width="623" src="https://cdn-images-1.medium.com/max/1000/1*mOyTxd6yXMOcm5ZrlsH0Jw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Iris dataset colored scatter plot</span></td></tr></tbody></table><h3><span style="font-family: georgia;"><br />Bar plots</span></h3><p class="graf graf--p" name="1c2b"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="27c7"><span style="font-family: georgia;">Bar graphs can be used <strong class="markup--strong markup--p-strong">to compare categorical data</strong>. We have to provide the frequency and the categories, we want to represent on the plot. </span></p><p class="graf graf--p" name="27c7"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="e3ca"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/02f8c0f7ec4b0e11bea0d300b3668959.js"></script>Here we are using the iris dataset, to compare the count of different species of flowers (however, they are equal to fifty). To find the count of each unique category in the dataset we are using the </span><span style="font-family: courier;"><code class="markup--code markup--p-code">value_counts()</code> </span><span style="font-family: georgia;">function. The variable </span><span style="font-family: courier;"><code class="markup--code markup--p-code">species</code> and <code class="markup--code markup--p-code">count</code></span><span style="font-family: georgia;"> in the following code store the name of each unique category ( </span><code class="markup--code markup--p-code"><span style="font-family: courier;">.index</span></code><span style="font-family: georgia;"> function) and the frequency of each category ( </span><code class="markup--code markup--p-code"><span style="font-family: courier;">.values</span></code><span style="font-family: georgia;"> function)</span></p><p class="graf graf--p graf--empty"><span style="font-family: georgia;"><br /></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Iris dataset matplotlib" class="graf-image" data-height="328" data-image-id="1*TK7ZOTEhLTsqX-I8eKxmig.png" data-width="522" src="https://cdn-images-1.medium.com/max/1000/1*TK7ZOTEhLTsqX-I8eKxmig.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Count of different species of flowers in the iris dataset</span></td></tr></tbody></table><p class="graf graf--p" name="9862"><br /><span style="font-family: georgia;">This is the most basic kind of bar graph, you can try some variations of this plot like multiple bar plots in the same figure, change the width of bars (using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">width=</span></code><span style="font-family: georgia;">parameter) or create a stacked bar plot (using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">bottom </span></code><span style="font-family: georgia;">parameter). <span></span></span></p><!--more--><p></p><h3><span style="font-family: georgia;">Box plots</span></h3><p class="graf graf--p" name="7d4a"><span style="font-family: georgia;">Box plots help plot and compare the values by plotting the distribution of data based on the sample minimum, the lower quartile, the median, the upper quartile, and the sample maximum (known as the <a class="markup--anchor markup--p-anchor" data-href="https://en.wikipedia.org/wiki/Five-number_summary" href="https://en.wikipedia.org/wiki/Five-number_summary" rel="noopener" target="_blank">five-number summary</a>). This can help us <strong class="markup--strong markup--p-strong">analyze the data to find the outliers and the variation in the data.</strong></span></p><p class="graf graf--p" name="a178"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/a3f9681e0131848964ef9656932a68c5.js"></script>We have excluded the species column here since we are only comparing the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">petal length, petal width, sepal length, sepal width</span></code><span style="font-family: georgia;"> of all the flowers in the iris dataset. We create the box plot using the </span><span style="font-family: courier;"><code class="markup--code markup--p-code">.boxplot()</code> </span><span style="font-family: georgia;">function.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Iris dataset matplotlib" class="graf-image" data-height="359" data-image-id="1*UR7bMB0sbLK70HHmj4H_sg.png" data-width="563" src="https://cdn-images-1.medium.com/max/1000/1*UR7bMB0sbLK70HHmj4H_sg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Box plot </span></td></tr></tbody></table><h4 class="graf graf--h4" name="540a"></h4><h3><span style="font-family: georgia;"><br />Histograms</span></h3><p class="graf graf--p" name="bcac"><span style="font-family: georgia;">Histograms are used for the representation of frequency distribution (or we can say probability distribution) of the data. We have to use the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.hist()</span></code><span style="font-family: georgia;"> function to create the histogram plot and we can also define the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">bins</span></code><span style="font-family: georgia;"> for the plot (i.e. breaking down the entire range of values into a series of intervals and calculating the count of values falling in each interval). </span></p><p class="graf graf--p" name="bcac"><span style="font-family: georgia;"><b><i>Histograms are a special kind of bar graph.</i></b></span></p><script src="https://gist.github.com/kk189/9d88c1bcde8b9b72c9fa04913ed18405.js"></script><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Histogram Matplotlib | Iris" class="graf-image" data-height="358" data-image-id="1*huQP4IewjcX4M9ySivRwvg.png" data-width="521" src="https://cdn-images-1.medium.com/max/1000/1*huQP4IewjcX4M9ySivRwvg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Histogram</span></td></tr></tbody></table><h3><span style="font-family: georgia;">Error Bars</span></h3><p class="graf graf--p" name="bcac"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="07a8"><span style="font-family: georgia;">Error bar is an excellent tool to <strong class="markup--strong markup--p-strong">find out the statistical difference</strong> between the group of data by giving a visual representation of the variation in data. It helps to point the<strong class="markup--strong markup--p-strong"> error and precision in the process of data analysis</strong> (and determine the quality of the model).</span></p><p class="graf graf--p" name="7db9"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/31f89be44d82cb7829188e3dca3599ed.js"></script>To plot the error bars, we have to use</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">errorbar()</code> </span><span style="font-family: georgia;">function where </span><code class="markup--code markup--p-code" style="font-family: georgia;">x</code><span style="font-family: georgia;"> and </span><code class="markup--code markup--p-code" style="font-family: georgia;">y</code><span style="font-family: georgia;"> are data point locations,</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">yerr</code> and <code class="markup--code markup--p-code">xerr</code></span><span style="font-family: georgia;"> define the size of the error bars (in this code we are only using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">yerr</span></code><span style="font-family: georgia;">).<span></span></span></p><!--more--><p></p><p class="graf graf--p" name="81d5"><span style="font-family: georgia;">We can also change the style and color of the error bars by using </span><code class="markup--code markup--p-code" style="font-family: georgia;">fmt</code><span style="font-family: georgia;"> parameter (like we set the style to dots </span><code class="markup--code markup--p-code" style="font-family: georgia;">’o’</code><span style="font-family: georgia;">in this particular example), </span><code class="markup--code markup--p-code" style="font-family: georgia;">ecolor</code><span style="font-family: georgia;"> for changing the color of dots and </span><span style="font-family: courier;"><code class="markup--code markup--p-code">color</code> </span><span style="font-family: georgia;">parameter for changing the color of vertical lines.</span></p><p class="graf graf--p" name="39ef"><span style="font-family: georgia;">By adding </span><span style="font-family: courier;"><code class="markup--code markup--p-code">loc = ''</code> </span><span style="font-family: georgia;">parameter in the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.legend()</span></code><span style="font-family: georgia;"> function we can determine the position of the legend in the plot.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Error bars matplotlib" class="graf-image" data-height="356" data-image-id="1*SdzR3JTqr5A7OCchqbBBFA.png" data-width="530" src="https://cdn-images-1.medium.com/max/1000/1*SdzR3JTqr5A7OCchqbBBFA.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) cos(x) error bar plot</span></td></tr></tbody></table><figure class="graf graf--figure" name="a801"><span style="font-family: georgia; font-size: x-small;"></span></figure><h3><span style="font-family: georgia;">Heat maps</span></h3><p class="graf graf--p" name="07a8"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="60d9"><span style="font-family: georgia;">Heat maps are used to represent categorical data in the form of<strong class="markup--strong markup--p-strong"> ‘color-coded image plot’ </strong>(values in the data are represented as colors) to find the correlation of the features in data (<strong class="markup--strong markup--p-strong">cluster analysis)</strong>. With the help of heat maps, we can have a quick and deep analysis of the data visually.</span></p><div><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/6dbbc8bd41ddb68779766337753b2f8a.js"></script>In this example, we are using the iris dataset to create a heat map.</span><span style="font-family: courier;"> </span><code class="markup--code markup--p-code"><span style="font-family: courier;">.corr()</span> </code><span style="font-family: georgia;">is a panda’s data frame function used to find the correlation in the dataset. The Heat map is created by using the</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">.imshow() </code></span><span style="font-family: georgia;">function where we pass the </span><code class="markup--code markup--p-code" style="font-family: georgia;">correlation</code><span style="font-family: georgia;"> of dataset, </span><code class="markup--code markup--p-code"><span style="font-family: courier;">cmap</span></code><span style="font-family: georgia;"> (for setting the style and color of the plot) as arguments. To add the colobar we use the</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">.figure.colorbar()</code> </span><span style="font-family: georgia;">function. And finally to add annotations (the values you can see mentioned over the color blocks) we have used two </span><span style="font-family: courier;">for</span><span style="font-family: georgia;"> loops.</span><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="heatmap using matplotlib" class="graf-image" data-height="371" data-image-id="1*5qTH748hFvmQIYc5lBUzfg.png" data-width="516" src="https://cdn-images-1.medium.com/max/1000/1*5qTH748hFvmQIYc5lBUzfg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Iris dataset heatmap</span></td></tr></tbody></table><figure class="graf graf--figure" name="4ffe"><span style="font-family: georgia;"></span></figure><h3><span style="font-family: georgia;">Pie charts</span></h3><span style="font-family: georgia;">Pie charts are used to find the correlation (it can be percentage or proportion of data) between the composition of categories in the data where each slice represents a different category, giving the summary of whole data.</span><br /><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/17fc91ed36ba626d2da738a388c13055.js"></script>To plot the pie chart we have to use the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.pie()</span></code><span style="font-family: georgia;"> function. To give a 3D effect to the plot we have used</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">shadow = True</code></span><span style="font-family: georgia;"> parameter, </span><span style="font-family: courier;"><code class="markup--code markup--p-code">explode</code> </span><span style="font-family: georgia;">parameter to show a category separately from the rest of the plot, and for displaying the percentage of each category we have to use </span><span style="font-family: courier;"><code class="markup--code markup--p-code">autopct</code> </span><span style="font-family: georgia;">parameter. To make the circle proportionate we can use the </span><code class="markup--code markup--p-code"><span style="font-family: courier;">plt.axis('equal')</span></code><span style="font-family: georgia;"> function.</span><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Pie chart matplotlib" class="graf-image" data-height="315" data-image-id="1*SAzqanvognLNT-jU0ZOOlQ.png" data-width="416" src="https://cdn-images-1.medium.com/max/1000/1*SAzqanvognLNT-jU0ZOOlQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Pie chart matplotlib</span></td></tr></tbody></table><figure class="graf graf--figure" name="b9a2"><span style="font-family: georgia;"></span></figure><h2><span style="font-family: georgia;">Seaborn</span></h2><span style="font-family: georgia;">With the seaborn’s high-level interface and attractive designs, we can create <strong class="markup--strong markup--p-strong">amazing plots with better visualizations</strong>. Moreover, <strong class="markup--strong markup--p-strong">the lines of code required are reduced to a very great extent </strong>(as compared to matplotlib).</span></div><div><span style="font-family: georgia;"><br />Code for importing the library in the workplace:</span></div><div><span><div style="font-family: georgia;"><script src="https://gist.github.com/kk189/30afce3e496358428c57cbd73daad18e.js"></script><br /></div><h3 style="font-family: georgia;">Line plots</h3><p class="graf graf--p" name="8f8e"><span style="font-family: georgia;">We can simply create the line plot in the seaborn library by using the</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">sns.lineplot()</code></span><span style="font-family: georgia;"> function.</span></p></span></div><div><span><p class="graf graf--p" name="a90c"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/e3a71c5dd5ca36c30ebfc19be93bf360.js"></script>Here we can vary the color of grid/background using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">.set_style()</span></code><span style="font-family: georgia;"> function available in the library. And using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">sns.lineplot()</span></code><span style="font-family: georgia;"> function we can plot the line chart.<span></span></span></p><!--more--><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Line chart using seaborn library" class="graf-image" data-height="317" data-image-id="1*W0O6TG5RGw0yrKdaGt8QOQ.png" data-width="479" src="https://cdn-images-1.medium.com/max/1000/1*W0O6TG5RGw0yrKdaGt8QOQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Line chart using seaborn library</span></td></tr></tbody></table><h3 style="font-family: georgia;">Scatter Plot</h3><p class="graf graf--p" name="5015" style="font-family: georgia;">With the seaborn library, we can create the scatter plot in just a single line of code! </p><p class="graf graf--p" name="4543"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/7f541e468056ffd09018d80650115547.js"></script>Here, we have used </span><code class="markup--code markup--p-code"><strong class="markup--strong markup--p-strong"><span style="font-family: courier;">FacetGrid()</span></strong></code><strong class="markup--strong markup--p-strong" style="font-family: georgia;"> function </strong><span style="font-family: georgia;">(with which we can quickly explore our dataset) to create the plot in which we can define </span><code class="markup--code markup--p-code" style="font-family: georgia;">hue</code><span style="font-family: georgia;"> (i.e. colors for scatter dots) and</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">.map</code> </span><span style="font-family: georgia;">function to define the graph type. (Alternative method for creating a scatter plot is using</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">sns.scatterplot()</code> </span><span style="font-family: georgia;">)</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Scatter plot using seaborn library" class="graf-image" data-height="354" data-image-id="1*-979m2VZqTigW1-KbU2hbQ.png" data-width="453" src="https://cdn-images-1.medium.com/max/1000/1*-979m2VZqTigW1-KbU2hbQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;"><span style="font-family: georgia; text-align: justify;">(Image by </span>Author<span style="font-family: georgia; text-align: justify;">) </span><strong class="markup--strong markup--figure-strong" style="font-family: georgia; text-align: justify;">Scatter plot using seaborn library</strong></span></td></tr></tbody></table><figure class="graf graf--figure" name="64e0"><span style="font-family: georgia; font-size: x-small;"></span></figure><h3><span style="font-family: georgia;">Bar plots</span></h3><div><p class="graf graf--p" name="26a3"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/d6d4f7ada471cce281dcbf8714a0fe3e.js"></script>We can create a bar plot in the seaborn library by using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">sns.barplot()</span></code><span style="font-family: georgia;"> function.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Bar plot using seaborn library" class="graf-image" data-height="317" data-image-id="1*NH_SP-GBLiR4Z9g0F2dLXw.png" data-width="472" src="https://cdn-images-1.medium.com/max/1000/1*NH_SP-GBLiR4Z9g0F2dLXw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Bar plot using seaborn library</span></td></tr></tbody></table><figure class="graf graf--figure" name="b2cf"><span style="font-family: georgia;"></span><figcaption class="imageCaption"><br /></figcaption></figure><h3><strong class="markup--strong markup--h4-strong"><span style="font-family: georgia;"><span><!--more--></span>Histogram</span></strong></h3><p class="graf graf--p" name="5e1e"><span style="font-family: georgia;">We can create a histogram in the seaborn library by using </span><span style="font-family: courier;"><code class="markup--code markup--p-code">sns.distplot()</code> </span><span style="font-family: georgia;">function. We can also calculate <strong class="markup--strong markup--p-strong">probability distribution frequency (PDF), cumulative distribution frequency (CDF), and kernel density estimate</strong> <strong class="markup--strong markup--p-strong">(KDE)</strong> using this library for data analysis.</span></p><p class="graf graf--p" name="796a"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/45150ba9100be2059808540fa07d7723.js"></script>Seaborn gives some more features for data visualization than matplotlib.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><span style="font-size: x-small;"><img alt="Histogram using seaborn library" class="graf-image" data-height="444" data-image-id="1*Ox3QR1EOJP9gixv08jW4Nw.png" data-width="550" src="https://cdn-images-1.medium.com/max/1000/1*Ox3QR1EOJP9gixv08jW4Nw.png" style="margin-left: auto; margin-right: auto;" /></span></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Histogram using seaborn library</span></td></tr></tbody></table><figure class="graf graf--figure" name="1697"><span style="font-family: georgia;"></span></figure><h3><span style="font-family: georgia;">Heat maps</span></h3><p class="graf graf--p" name="640b"><span style="font-family: georgia;">Seaborn is very efficient in creating heat maps by significantly reducing the lines of code to create the figure.</span></p><p class="graf graf--p" name="9e65"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/72947deb3c49133e4b9ea0a58b91fbc3.js"></script>Multiple lines of code in matplotlib is reduced to just two lines!</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Heatmap using seaborn library" class="graf-image" data-height="321" data-image-id="1*QhghJs5KUhDBGu_k3bNopg.png" data-width="557" src="https://cdn-images-1.medium.com/max/1000/1*QhghJs5KUhDBGu_k3bNopg.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Heatmap using seaborn library</span></td></tr></tbody></table><figure class="graf graf--figure" name="39e4"><span style="font-family: georgia;"></span></figure><h4 class="graf graf--h4" name="588c"><span style="font-family: georgia;">Pair plots</span></h4><p class="graf graf--p" name="8361"><span style="font-family: georgia;">This is a unique kind of plot available in the seaborn library. This plots a pairwise relationship in datasets (in a single figure). This is an amazing tool for the purpose of data analysis.</span></p><p class="graf graf--p" name="8361"><span style="font-family: georgia;"><img alt="" src="data:image/png;base64,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" /></span></p><p class="graf graf--p" name="de21"><span style="font-family: georgia;">By using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">sns.pairplot()</span></code><span style="font-family: georgia;"> function we can create pair plots (</span><span style="font-family: courier;"> <code class="markup--code markup--p-code"><span style="font-family: courier;">heigh</span></code></span><code class="markup--code markup--p-code"><span style="font-family: courier;">t</span></code><code class="markup--code markup--p-code" style="font-family: georgia;"> </code><span style="font-family: georgia;">is used to adjust the height of the plots).</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Pairplot using seaborn library" class="graf-image" data-height="740" data-image-id="1*_VqcnV-YdXAQBpbwIavFdQ.png" data-width="827" src="https://cdn-images-1.medium.com/max/1000/1*_VqcnV-YdXAQBpbwIavFdQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Pairplot using seaborn library</span></td></tr></tbody></table><figure class="graf graf--figure" name="4a25"><span style="font-family: georgia;"></span></figure><h3><span style="font-family: georgia;">Pandas Visualization</span></h3><p class="graf graf--p" name="006d"><span style="font-family: georgia;">This library provides an easy way to plot graphs using pandas data frames and data structures. This library is also built on top of matplotlib thus requires fewer lines of code. </span></p><h4 class="graf graf--h4" name="e940"><span style="font-family: georgia;">Histograms</span></h4><div><span style="font-family: georgia;"><img alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAzEAAACACAYAAAA76OfNAAAgAElEQVR4Ae2dvYscyfnH/Zd09ssuPVZgcGBwcOBASi6xWUscWItDJz58CB+7sMjgFRhH5qxZzCF8gR2sjFmFBoNgDScUDI7mf6kfXS9dL/1UdfVMz0vPfIJldrqrq55+6lPVz7deen7QNI3iDx/AAAzAAAzAAAzAAAzAAAzMhYEfzMVQ7KRRwQAMwAAMwAAMwAAMwAAMtAwgYpiJYiYOBmAABmAABmAABmAABmbFACIGYGcFLKMvjL7AAAzAAAzAAAzAAAwgYhAxiBgYgAEYgAEYgAEYgAEYmBUDiBiAnRWwjLww8gIDMAADMAADMAADMICIQcQgYmAABmAABmAABmAABmBgVgwgYgB2VsAy8sLICwzAAAzAAAzAAAzAACIGEYOIgQEYgAEYgAEYgAEYgIFZMYCIAdhZAcvICyMvMAADMAADMAADMAADiBhEDCIGBmAABmAABmAABmAABmbFACIGYGcFLCMvjLzAAAzAAAzAAAzAAAwgYhAxiBgYgAEYgAEYgAEYgAEYmBUDiBiAnRWwjLww8gIDMAADMAADMAADMICIQcQgYmAABmAABmAABmAABmBgVgwciYg5V7fvl2q5XKq7q82V6eVbk9fD6/PNKvOLN+rjaqVW3d9H9eYL2b6fP/2V+vDHL9V/nn6yWZk7boC/+/WX2u7W9vbv7z+V769qxOSnP4vy+vDrH87PF1efqZ9X1sFQnT/79mPAzkqtvn+jnlXmXeXvNK+nt+phadhv29Ly/a06T9Ps5fululsuVXV77O7jTl3u3N5n6s33K/Xx22cxu7ovyLf/tepr5/e2QdvG1pgH/IE/YAAGYGBjBhAxAkRTi5j7l8MP/6GAduogR4uPEQH3YPmffqb+s6mI6eriE/XN1Zfq0ETMkM+Gzqc+HFPn1+92IGI6/zdKt4EJRMw0+exPxIy1X9fT6l5dB7509V4659LwOdxX4iN8BAMwAAMw0DJwACLmTF28WqjFYqFunp9trMoOCmw7E1MjYho7C7HRTIYQOOX8MTbgzuXTHUfEqNE+HVHniJgRMzEj2kHHb+aaMSLGzJyVZlvMLM3WZ9Qy9zJ0r5wnKIABGIABGJgTA3sVMWfPb9RicaMunlyoG0TM5suxRgQvowPuobwRMYgYgZExIiDfcY6ciRHsyOddfmDV23+t7lfCMrLUFjuw0Vtulqbj+3ENaFGf1CcMwAAMTM7A/kTMkxdq8epCnbWV+mhdEeP3wug1/MsHdfu0H5Scv35Qy2W7Pj5JnyyX0QFLsCcgv7/GBFWmzHYPgVxuM2YmpgC3Xnakl379UP3d7j3J7p8p7SuxQsPtX+l9psvL0rz++DP1u5ydOxYxbimWu4d0L9EYn2lBF/jV5amXs43wWScMk2ummF3baCZG7xExjEaMJ/yHgf5Q8G7aVLB/5u2l75y6PSnB+aBdjd9r40VMXG6y5yUtt3B/TZpW9w+27+idS+5DyNfMwsjLyEK/tv+zrKzfR6c+4js+ggEYgAEYGGJgfyImDIbXFjFBBV/dZcVEGPj4zcFWiITBl7PJBjGyiJGuu1R3QmAzqYhJN84LS5FMYP8r9c2nzi9W9Agb5LuA291z+tkG4tF1dp9KTsjsUMQY0REIKlt2KGS8yAn8Ifisl5dNE+blGtGQz0xe7QsOfJnGjsDW1M+V3zcXMTYQ73iXOHbclPbEuIGAUEDYvIQ2MCSGnG/Lnzb/6GUD1o7ufrztbV7Fcm379n1Bo7SoEfIq5tPVnV0m9u7aC7nuXGyXvs+X9/qFDVXLTEv5cK7O3/gJP8EADMDAUTJwUiImFSXZAKUkYkrn0kYy5UxMb9N8uvndCJZe8K2Dch9Uu2BxKCB36aJPQQR053clYjLlpGLBiZh4FiT1Ufq9DTitX9NZqaYZXC5mREzi64y9nd9SZjLfJxExSZDuZyj7gXa2beQGC/Tx/tsBs/lk7lP2iyyS1rK/LTd3D4JNVfaPbueVS88Ee2T/9OuPdPgEBmAABmDg2Bk4IRGTWfIlBQpFoeJHhVNR1INldHAjN7g0QHflREIkI1aaTBAdXSv5QDqWyUvbUzon5VU8lgo075ecL8yLEbyAkNMlosXaHAu/fNlDPhPPT+SXKURMj9dCMJ8L3vXxcOlVV4+mXUSzG0MzIt21vn4d2/GnnPfaIsa27+wy0MCunB8i+/TMSmlDf3p/I2duAnuicjl+lCOL1HHaXvgOEzAAAzIDiBgpECiKmNaRbkmNWyufEUg7FDFu5qHbz5Hs8YhnJIZnFdoGo4PyJJ82/zQv3bgmCtZNQx0QEoJN5r5HipjuHoPlXoXZJlGkBPyI5yfyy0GJGGHZWNNYcZ/M9lSJgMCHckc9sYjR5Vl7u7064fI431lW2Y+IQUwMMuyZkhnnPH6BARiAgbEMIGKkh8+giAlAK43q7lDEpDMRQyCIAXfgCyNgvCjQ+ZUC8tK5IN8hu8z5vIiRZ1iC+rBlyemSmRi3dCwRRaJIc4JHWGbm7kn06UR+mYuImcVMTMqjXQpnXv4Rs4SIif3hWOcTv8AADMAADOybAURMGtC038eImFL6rYqYJCAfGSzLQb5rkCbv3o9NlsoonXM+tmk+5F4O4NI5cRG9WMDalls2111r0sn3l/isMi/XSOU8nc8ys1s1fklsd+WFn4MixgXi0ixJhudSgJ49l1uCljleWvIV3p/Zp7JU8pvLtjET4+tN27GJ/aPbOXtiorqv4J/0Ca/4jNk/GIABGDiEH7vc5BXLQceeCULah58JpDJLviQIMkGfC3bS0eZs/qODm+B+Aruk4NnMlATLoNxMQfBmrOKD3y6biveDuPLtTEgoNmz6TZaTmfto396VWZLW3XN+JqbbeB/a1l3n7G+U5LOmSUTMWIFR9Nl+RYwWHXpplMC5xLNuL/2N+I6ZrIhxSykjsSQvJXPtpX0VedpmXDnus2i/XaqW5lESSHn7TX+Q7g8y5QtLyqyf0rKd3eZz5B4X3k5G8CH0WTFTvi/jOL6AARiAAZmB/c3E2NcqLxYL1f+7URePZIOjirTBmf+9FrdHJR7RzYqM6EGSrpEP8ko2Mpv88uc7GycVMSb4N3s/vlQfMsuaQqEwOm2Up52NcUut2nO9oD9J49Lqz1hgaZ8MzMQYYZbcp80zXeIlpg1mbqpETOPEjlBm5AvPYs+/Qbq9LiezwbY4kyG2k77Y6XPtGY+D+HRPWFmk9PKNBJD1bcn+ShHTK6fb79K3z4sme4+STbZ/6OUrpB39OzHfv1HPov7HM9b1H5xH7MAADMAADMBAloH9iZhTqJRJRYwgCk7Bh1u8RydIUoHk3ujWW063RVuGAtfB5WQl26SZmFJ6zmU7zHw9mSViq6HfipmoT8jbgRjCNzAAAzAAA6fBACJmmwHbRAGLPKtwGoBusyOSluSZ8uwMUzCzs007avJGxBw+72Y2ZqXyP2Jpl50xC7OGSDz8+q9px6ShHmEABmBgOgYQMYiYkw0ocjMxbqlab4Zmm6wM5I2Ima7T2+YDRNfTSv7NGHPuXl0P1PU27SPveXBEPVFPMAADMDDMACJmmwGFnYlZrVbK/MnBzRCozMQMgzzkw9x5J2S6/UO5/Tzb5CSTtxvZ7/hZdwSf5WQ7FOpmtuXjt8/iMnVfsF77z7HL8e31C/gW38IADMDA4TOAiMkEkMB7+PBSR9QRDMAADMAADMAADJwmA4gYREw8Yow/8AcMwAAMwAAMwAAMwMCBM4CIOfAKYnThNEcXqHfqHQZgAAZgAAZgAAbyDCBiEDGMNMAADMAADMAADMAADMDArBhAxADsrIBlRCI/IoFv8A0MwAAMwAAMwMCpMICIQcQgYmAABmAABmAABmAABmBgVgwgYgB2VsCeyugC98lIGgzAAAzAAAzAAAzkGTgNEXN1p5bLpf97e3kYgfuB/H6H/j2S3m+QZH7vAtF3GOxQD9QDDMAADMAADMDACTNwGiKmq+Bzdft+qZYFEXP++kEtl3fqsrsmrwA3VseHIGJe3usf4rx/Kdxn6dwu/EMZdM4wAAMwAAMwAAMwAAMCA/sVMY8u1M1ioRbd3426eCQE04Lh6wkIREzkN/0r4ivV+3XxwN/mV+P5pfHIb4F/OL7N9kre8AUDMAADMAADMCAzsD8RowVMLFoef9UKmvjYtBWHiAn9ef1upVa9ZWQpKGZZ2XC69Dq+h77mf3iAARiAARiAARiAgekY2J+IkUaz7czMzfOzumkzvRzrQd0+bdTl22DPy/tbdS7l3+REzKW6C/fM9P5fc3mZXS7m9+Mk+QTLyYbtT200960bQ5CP1DjEJXJ2FkZcRpb6jmVldTymfuM7foMBGIABGIABGICBrTBwBCLGipdun4sN9rvvoeLLiRifRgz414HPCouH1+e+4tpjoV02jRY53XFjf3SdfTFBeMzYuVR3V63twjWBzTptIuzMMrF7dR2kkwSQOXat7lcrtXp37e+l6jrv13zepME3MAADMAADMAADMAAD4xg4LBHz5IXeH/PiSeVNOBHQCQBzXV6I7FDEaOERzJZIQX/Gfj0r04mOnM32uE5n/u9ETlK2zi/ykV0iNkKU1C09q6w3yRccQyDCAAzAAAzAAAzAAAxUMnBAIuaxeqE3+L9QjyuNb6wIMLMRQQCdBPFe2eYEgb82L4B8Gp9f4ZgTKMuCkMnYH4kYm6YTKIFvQltDoWKOL5W5RrpnM7NS2tCf3uO4mZuCXwL70zL4jt9gAAZgAAZgAAZgAAZqGDgQEeMEzMhN/RkR0ByCiNHBerqPJb8nJqwsScT0hFrTKCNWjEjS/3ezMg/q9vWdfZV0Mkuj7ULEhP7mfzpLGIABGIABGIABGJgXAwcgYs7UxSvzmuXqZWRuNP/gRUwAgxZW7f6dQMhk7B8nYkx+flbmUt21Ykbn3Z4zQioWQYgYOqqATdee+GQKHwZgAAZgAAZgYCYM7FnEbCBgWgfXiICoIqSlVUkwl53FSdLZsiNREpWVpG/PpXlX2Z+zOTmu875Tl+2n3v9izt9dtSImXdLGnhhEjMDnEL+c58EGAzAAAzAAAzBwIAzsUcRsKGBaB0oiQAfz7q1daaCWBP5SJThxEm2ET/NxS7nMm9HiWQ6Ttp0ZSY/rGZaxMzGtjcI9hUvJdEDu7F76ezdpktkfe8/j9rjwdjJET78N4BN8AgMwAAMwAAMwsC8G9idi7JvIFnozv1lO5v+v3BsTBO7+t1jSWYfkN2SS34BJhYauCCsafJ7BEjAnfLqyhXM2jREt9hXQbbndG8cs8DaP1IZoOZkrb9Amt/8msMfZmJbb5jn6d2I+qjdf0FD31VApF/ZgAAZgAAZgAAZgwDOwPxHjgvNNPjMigAr2FVzyhX5t8mrot2LGLz0rlcm5urrBT/gJBmAABmAABmAABvIMIGI2EVFzv9bOxpR+xNIsO2MWhk4k34ngG3wDAzAAAzAAAzCwawYQMXMXIpva//JerVYrJf5mjD13/5KGueuGSXkwBwMwAAMwAAMwAAN5BhAxm4qAI7hez7Z8/0Y9i+7FLCMTxU2ULg8XDQ/fwAAMwAAMwAAMwAAMbIOBeYsYgmle8wcDMAADMAADMAADMAADJ8cAIgboTw76bYwGkCejTDAAAzAAAzAAAzCwOwYQMYgYRAwMwAAMwAAMwAAMwAAMzIoBRAzAzgpYRjh2N8KBr/E1DMAADMAADMDAoTKAiEHEIGJgAAZgAAZgAAZgAAZgYFYMIGIAdlbAHupoAHYxUgUDMAADMAADMAADu2MAEYOIQcTAAAzAAAzAAAzAAAzAwKwYOBIRc65u3y/VcrlUd1ebK8DLtyavh9fnh1GZT2/Vw9LY1N7j8v2tOj+Ihnap7pZLVe2n7j7u1OXO7d/+795obqaqm85Xtt6nynfnft+8PR70qBb1VOwj5d+g2gcT22//RU6v7vTzSfffui9P+8Bpn2FFW7o+YB9ljq37a3W/Wqn5/OByJWdfvFEfVx/Vmy/G+oP0dWzjp1PxEyKm69A99AcnYgIbpwqUp8lnfyJmrP3X71ZqtbpX14Evp27kY22qLX+qfKfKp9buU0s3X/+ObMe1bejlvVodUgC6L3usgCkP9uxDUOyjTP+cHe4fjCBY9X6IeUwee0hbydkunknDPt6Df2r7D9IVB4ioW8PuHkXMY/VisVCL6O9GXTyiUZXgnCpQmiafLQU/FZ3XGPv1aPAORr3G2FSq4/TcVPlOlU9qX//7lfrRX/6nPi/8ffbLXxxdB707/07dR26hHeuR5pX6+O0zsZ5/8udv1L8+fOf//v0b9ZOKdt9nrVHN+W/U38K8Pnyjvj6XfbSrviC0U3OxTGdeZPvC6079/3KQ/2P19b8Dftr634Ch5/9I8vrwe/V8XR6bRtVxNlORtoFfTp1p7n/6fm+PIqZ/M4+/akUNQqYE+lSB0jT5bCH4qewg6+03yxFywVTJ12PP1dvUZ79U1lT5TpVPyVbp3P/98r/q87/8U31SWbdSHnM4ti//bu6b6duxDkAzI+hawEQB5zP1p3WDUC1gYtFiAtL4mPfR7gPH+XIxrp/yPp7gOiuC5WVklpd/hAJ5XYasGIp4dAJpEyFTydmA2J/Up0fe/+KrCdrdDBk5KBHTPLpQN4uFevGktjL8dLhZa/ygbp/2rz1//aCWeiQsSZ/sM9APm2DvSX5/jXno+/XNcrmDjUqvpzfXRmUndoX5DD0Qzb0G+2feXvqR0HT9fnCv6+218cFPXG4y6piWW7i/Jk0bjmD2zgX3mdkrZEbEysvIYtuXahn6rG3UttyUh7Quuu+JnfF1xmftsbjcxGdBZ9LlGxwLmXD2eR6DvBJbfJrcXpuJ2A5sLYkYf+4X6tM/BLM3f/ir+r8uDzOzk87e+GvDNp/k85f/qk93NLs7VE/6fNjmIs6s36W2YZcjRRz16jWoc+033zZDVgxzJm3MX9KWxux16+rJ1kMxAA3ryv9vZmZywsOnC+8l+7+dmfnbn3/s+77QxsrlPtn8w7wq/i9zkTyTloVnSa/OXZ3F1/TqNeKs9WVdmT1eO3ZT1hrVpHt+emWOq8OSCG6+/r36lzDbZhgaKTwsK3/6OrEvU8YoJio5K884JXZV8DbKRvKT+wj8Mhu/zFzEBA1cd6JxZ+4ac9ip+3XJNmiQOlv7sIiChg5q6bpLdScFH901gZ3hsfCh1Nkh5e+vzz8Q3YMpfMDYvATb8vn4spz/8p82/0hAWDu6+4nzK5Zr/eHryAoIIa9iPp2P7WjYu+tsgzRsxNy0x6K6z/CQ2mC+P6iHIBDp51/wmVBPre/TcsL6cGyH9ur0gQ0ufSkfk0Zib022uzpolCw2DBfmnBEvXqTY5Wi/vbL1Viti0utc2bsRMiX/5pgKBXOfFeMjU5++XdfVuanLqC01Tjj7vMJ6T9M6bsZ+1gwcpHnuVMQ0ZnZ2VegXUvvGfnd11Bs0sEJA9HXhGeYGKvx1Un/v6negPwvaphEfcfr8vRqmwr6mTdvnVupH4udAvow23cDsuRYY36lUeOgZuGhGpaLMjOBdSxCFftX/V3JWKXbKPqu41559XINP58/AAYmYM3XxaqEWry7U2TqNrfAAcA+UtPPNBh2ZoFUDXzo31m6bVxjItGUYe9NAw8CWtTl3//p4/61t2XxG3YN9WCXB91r2t+Xm7kGwqcr+ihHhqnwydZ5eq7/3xEP6QJd9Vrr3tBzf8Zq8fGDjOqS0zAF2nH8z9+nLc3XkRoD7n2kba6+tETE/euxsN5+f/PZ/6vNuNqZOxMjl2JmZThDF5UT35vyw5me+nuQyDS9BO7f+j+szreP0u8s7rXM5ndw25bTeNzbvbiQ+qfdokGF44MDn62xvlFkCNnIUPVdPmSA3LLc40p/Ld83j1VwU+j+x3oS+vbosdy+FMkN/tf/rvKO6busvw06UrxNcCTeOp+T50eigvvzmLreHxc22lZcQes7Se2q/G8HynfqXXZ7mvqciSbp26FgdZwOizdUVn9nByKF64Hy5DczZP3sWMVa4dJv7X6jH6zbUqNOMK8w8AGpHm/LLh0xF+we6FLCNgiEXNBbuJfeQ0sfDpVedH+WHTC6fUfZnHmDiA9faUyzX+mPZEwJxfbY2FvNx917xMDS2Dry2OlNPqQ3pd+NL+wDvHtRyfbiRVokpOV8vKGqvyebj/GXrsx09lvIcx4avM1lcmPPm3NBMSY2IyYuVWBB5u9a9n9x1w/6Ny+63k5QVV8dB36X7Brl+4vJlzvpltjbJaXP3WT6+RkBmRYcLIsv5xz7sp7V7IwY2Zq8zW9Qva8gWcz6ul8I1Q/1+2r8L6av6s669C3yF58L/hbK0P3LHM31mjQ+r68Zxo1/qsKEAtjMy5mUTEy1r7Db4l5czN816wr/Gl6QptLeQb/6frUDcs4hJAHvyQr+t7Ob52XiH5jrTbmYjCASGgB3sgNORpRF5h2XnyincS+6BmDvuApR0tiefPqmT0N7e/3LwIwdKJt/hck2efhlGMFIdlD+cT6NqRvT8gzgYJUxHGzP1lNqQfncPkPi47LP1RYzMni4zCXpiO3L1PBHbQV3tRsTYpWS5N6J1szq5+978eNm/KdeOt4TvpO3rPEMek/OOsfYzrnOZM7ltymnDvOv/HyliXCA6dhlQwJe3zQmY4SC0OlAWyxnHSpmLIK9C3br+wc/SCYLX2arzcXwJe/xcuvazVGaXLs9HJ5rcrEryuc5gSE3duJkYM1viNuJ/p9zMjGci8G93P/ExN/PirnV5TyGqa+4FERPXx5i6Iy2+OywR0zRKv6FsnSVlhc7YdLRysCc2gkzQWkpbM3vQuz5XTuFecg/E3HEnYvzDz0CfTz+mUcgPNzlQWqPc7mGcBHouYOtmNzI2V8zEpHWi/dI+iMPAMVNPqQ/T7yZvG2x0+ck+c0GK9NCX8y0HINI10rH0/qPv9r4jtrs6CYKkIHCR7N+NiMnPxET3lAlkpkiT96+p8/RHauV2EvJh/o98Wt03hPn49jFcpk/rfWLtD+rZDzIkbWVoP0Po/270e8NRdJ2nD2RrlgHVBZeSL8Yfy3OR5FWoWyM2kjY31P+5fjLtz8I6KJVp0xXtr7i+/yKB8n0M1Y0THWk9r7WkzIpoJ2Ac87ky3Pnaz6F7MfkwE1PrT9IlfUbYlk/0f0SMVPGZoDXbgErpdSefWa6Uua700Mieyz1MMsflYEZoICX7M8tQSnln7ZfqoT22if0Ve2KkOu3ZKNaTDeyCQKJ3XWu/vdaLyDHBpakPMV/tLzmvnHAt1YvkB31MvHeBk1z9Ve2JWWc5mXsLmX918+hlY3ZD7VQ/ppetJ9uGIjHSzRD3Bbqup5YrfV16vrbOpXRuli3NMxXa4+o3ZqcyIBshYMzbm/K/OdM04wRMa+/QXoXhMut9lOUibTOZvq4TAd1ASH3Z7b0Wy8+WacsYOj9B/xDzMzyDnhMrWeFhnwPSjx1nr8mIm56taR0m34c4M/mNnL1MyhhrE+nHtR/8ddj+OigRc/b8Ri8nq3/FcuDcQmdrgreJZmKu7pQPSE35pfz1A0SPYArlSw8AfR/ymve2MeUfSNLyAhPIRLMKrgO05aT3kjbYov0Ti5jWj2mgZ8pPgy4nbpa9uojtHwqoWp+leUs+S4/Z7229FkVMvk4iv/eETsB1sc7dSyBittb22Ui2Y1/HNofnNp+JsYKlWxLmBEz7VjMvYppHf1WftcvJKjfxm1HS1WS/Kp9tm7Z+w3bo21XKnxe+7WxHxIltu1J/06/zlD37XfdF/TLN9TFHYR2O+X9w9HmEgOmW2qzaepL2FowXMO7tV/m3k9l+I1tmnnXJT1kuXF/sPgvPsLo8avuzwP5CmbnBkPQep2TH5D0Q1LsliNLvxEh7odxgxUoQwo7FaDmjY2p4WWLqi/i7uY88Z7YerH3yb+IEdeU44XP8dgN8drQ+25+Isb8Js+g29a/xZjIXHOgHc36KWnrox51N21EEgWkvv/ihb/ILy4vPR3lbsRAGu9150f5+INEvz5cdBzlhoGLSxOfjDrGXbxCQdzaW7K8UMb1yAv+m9pkHor8/0W+2Q+rlK9g/GFAJ9Z7apH2R1FUrtnT5YZnOV8H9hYGr8anMWSreevcW5Jna10sb2pR03kNpe+eTfTUdF0m+peObi5iW23jPi34d8+N/xiJG2xSn+9zukUnffmbstUHGaqXWDSD6/vLsRvWUsNGeM9dKfYdrx/2+wPm5V65Y5zFr2h5tR6nMjP0j6rsZmAHt9hzoDdnJL6VHwaTtr7ogVBAxLqCV8hJ+S0T7T+dXfvuV2U+XE05xP+rqJPdZFCBJvxIt04vqNK7LMF3cd/TTRRy29VhZZq8vDvqguEw3mOLZ0fZF9o/z2eAMhlTvEjuaWy9KxR89dkImYmiCJY41nFXMCua44vg4pvDXcfprfyJmzEPxWNPah0n6QKCxTdnYKkfDdsaYCTJ6gcXOyp/St/PPyywbGghod143VsRsEATuuw8xfhVEx1q+tG34+zfq2VrXh5wOzc66tFOW6fJc99MKE4GH6WdB1rVx4uusEBZFxxoMuFnXdQcrxrenSs4GBP/4cieuhzV8jc3UwS4ZQMTss5EiYnYyxbn7B1ipE0PE7LKDK5ZlR/inCpSKZY3pZ46hX3D7ECb4QckphabpC4ZF65RlbsyF5UEa+DAzcvkZu43LHsPtxGknq4MJWaz1Zx1nVuhMIs5LzxzO1dYb6ebHCiJm4o53VCM4hmBln/4bUfZkD8QRZcosIGJkv+yy89x8Gdn27sGOuq+5iXt7dq1RP5uKRHu9vBdmfXuKo/FTl7lxf9HeZ2Ymxj4/SktuD4qH0b5wy8DWndFz1wt7YUbbMoI3y1CRM7eMTNznNaKsbd4Hee9kkHXebXT/rCJi9tlQETE77CTMA23/o+6IGDptoeN3AWm77+AYBIztV/WI9EGMNB9K+4GFatsAAAfuSURBVBfqvuoZZIVMsC8l9+KH42pfZsBhSBAczj1XcqZnh4ZnBA/nvtblluuow+0ygIipeoBstxKAHP/CAAzAAAzAAAzAAAzAQD0DiBhEzA5nQ+rBpBHjKxiAARiAARiAARiAgRwDiBhEDCIGBmAABmAABmAABmAABmbFACIGYGcFbE6Nc5yRGhiAARiAARiAARg4HQYQMYgYRAwMwAAMwAAMwAAMwAAMzIoBRAzAzgpYRlhOZ4SFuqauYQAGYAAGYAAGcgychoi5ulPt6yi7v0N5hemBvGJZfg1q5asiEYGIQBiAARiAARiAARiAgR0zcBoipnPqubp9X/4dBvMLyHfqsrtmiwr4EERM6Ye5Sud24R/KoEOEARiAARiAARiAARgQGDgYEfP4q4VaLBZq8epCnQmG5qaSxh1HxET+0j+4Vf41Yz1Ls+JHuSK/bY3PLQpmbOYBAAMwAAMwAAMwcEQMHIaIefLCCBhEzE4b1/W7lVoN/pq2WVY2nI4AHKEDAzAAAzAAAzAAAzCwGwYOQMScqYtXC7X46rHSszFjZmL0cqwHdfu0UZdvgz0v72/Vuag0czMxl+ou3DPT+3/N5WV2uVi3F2eZ5BMsJxu2P7XR3LduKEE+UsMRl8jZWZj7lxWgsaxsp+JSqkOOVXAqtnmugx0YgAEYgAEYOEYG9i5izp7fqMXihXrcNGuKGCteus36Ntjvvofg5kSMTyMG/OsER1ZYPLw+9wFweyy0y6bRIqc7buyPrrMvJgiPGTuX6u6qtV24JrBZp02EnVkmdq+ug3R5wK/V/WqlVu+u/b1UXef9ms+bNPgGBmAABmAABmAABmBgHAP7FTGPLtTNYqFePDFGrzcT09+onxciOxQxWngEsyVS0O9ETCdgjB/0rEwnOnI22+M6nfm/EzlJ2Tq/qAy7RGyEKKlbejYOPhor/oIBGIABGIABGIABGFiHgb2KmFS0pN8Hb8iKADMbEQCQBPE+n5wg8NfmBZBP4/MrHHMCZVkQMhn7IxFj03QCJRBDoa2hUDHHl8pcI92zmVn5+O2z6pmVcTM3Bb8E9lf5kfTVdYQ/4Q4GYAAGYAAGYOBUGNifiNGb+W/UxSMP21GJGB18p/tY8ntiQuAkEdMTak2jjFgxIkn/383KPKjb13dqqWdfklkabRciJvQ3//s2iC/wBQzAAAzAAAzAwBwY2JOIeaxeLBbq5vlZNMp8fCImaAR2X8sy3Nw/YiYmL2KMMPKzMpfqrhUzOu/2nBFS8fWImDk0TmwM2g8zclFfCRuwAQMwAAMwcOoM7EfEhK9Ubl+rLP3VvKWsRgREwY+0tCppBNmlaEk6W3YkSqKykvTtuTTvKvtzNifHdd536rL9DGZg7q5aEZMuaWNPzKk3fO5faJ9D7ZfzCCkYgAEYgAEYOBgG9iNiMgBMMhOjg3n31q40UEkCf8kOJ06ijfBpPm4pl3kzWjzLYdK2MyPpcb1MbOxMTGujcE/hUjIdkDq7l/7eTZrWxmQZW9OocXtceDsZQX+/DeATfAIDMAADMAADMLAvBo5CxPjfYWkD9nTWIfkNmeQ3YFKhoSvCigafb18ENJ1oEM5ZcWREi30FdFtu98YxC3zVTIxNO2iT238T2ONsTMtt7Rv9OzEf1ZsvaKj7aqiUC3swAAMwAAMwAAMw4Bk4KBEzumIyImB0PtKMzAkc069NXg39Vsz4pWf43zcwfIEvYAAGYAAGYAAGYGB6BhAxJyBWsg3HzsaUfsTSLDtjFibrw1Pmh3s/mHXB8Dn9wxGf4lMYgAEYOGwGEDGnHoi9vFer1UqJvxljz92/PGyI6WSoHxiAARiAARiAARg4LQYQMacuYtwm/+/fqGeRL8wyMlHcROlOq8HQQVLfMAADMAADMAADMLB/BuYtYgimWc4CAzAAAzAAAzAAAzAAAyfHACIG6E8OekZP9j96Qh1QBzAAAzAAAzAAA5swgIhBxCBiYAAGYAAGYAAGYAAGYGBWDCBiAHZWwG6i2LmWER8YgAEYgAEYgAEYOA4GEDGIGEQMDMAADMAADMAADMAADMyKAUQMwM4KWEZPjmP0hHqkHmEABmAABmAABjZhABGDiEHEwAAMwAAMwAAMwAAMwMCsGEDEAOysgN1EsXMtIz4wAAMwAAMwAAMwcBwMIGIQMYgYGIABGIABGIABGIABGJgVA4gYgJ0VsIyeHMfoCfVIPcIADMAADMAADGzCACIGEYOIgQEYgAEYgAEYgAEYgIFZMYCIAdhZAbuJYudaRnxgAAZgAAZgAAZg4DgYQMQgYhAxMAADMAADMAADMAADMDArBhAxADsrYBk9OY7RE+qReoQBGIABGIABGNiEAUQMIgYRAwMwAAMwAAMwAAMwAAOzYgARA7CzAnYTxc61jPjAAAzAAAzAAAzAwHEw8INHjx4p/vABDMAADMAADMAADMAADMDAXBhAxCDiELEwAAMwAAMwAAMwAAMwMCsGEDEAOytg5zI6gJ2MZMEADMAADMAADMDA9hhAxCBiEDEwAAMwAAMwAAMwAAMwMCsGEDEAOytgGdHY3ogGvsW3MAADMAADMAADc2EAEYOIQcTAAAzAAAzAAAzAAAzAwKwYQMQA7KyAncvoAHYykgUDMAADMAADMAAD22MAEYOIQcTAAAzAAAzAAAzAAAzAwKwYQMQA7KyAZURjeyMa+BbfwgAMwAAMwAAMzIWB/wcJtmjGoOyuRgAAAABJRU5ErkJggg==" /></span></div><p class="graf graf--p" name="8361"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="ec60"><span style="font-family: georgia;">It is very simple to create a histogram with this library, we simply have to use </span><span style="font-family: courier;"><code class="markup--code markup--p-code">.plot.hist()</code> </span><span style="font-family: georgia;">function. We can also create subplots in the same figure by using</span><span style="font-family: courier;"> <code class="markup--code markup--p-code">subplots=True</code></span><span style="font-family: georgia;"> argument.<span></span></span></p><!--more--><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Histogram using pandas library" class="graf-image" data-height="727" data-image-id="1*2KzTOQlqsFY-oOIZJRlEsw.png" data-width="507" src="https://cdn-images-1.medium.com/max/1000/1*2KzTOQlqsFY-oOIZJRlEsw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Histogram using the pandas library</span></td></tr></tbody></table><h3><span style="font-family: georgia;">Line plots</span></h3><p class="graf graf--p" name="8519"><span style="font-family: georgia;">We can create line plots using this library by using </span><code class="markup--code markup--p-code"><span style="font-family: courier;">.plot.line()</span></code><span style="font-family: georgia;"> function. Legends are also automatically added in this library.</span></p><script src="https://gist.github.com/kk189/5274500de83cb167e6927bfe34fb6eb9.js"></script><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Line plots using pandas library" class="graf-image" data-height="316" data-image-id="1*i57QwlDPWDRX5MawW1hPkQ.png" data-width="471" src="https://cdn-images-1.medium.com/max/1000/1*i57QwlDPWDRX5MawW1hPkQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Line plots using the pandas library</span></td></tr></tbody></table><h2><span style="font-family: georgia;">Plotly</span></h2><p class="graf graf--p" name="72bd"><span style="font-family: georgia;">With this library, we can create <strong class="markup--strong markup--p-strong">multidimensional interactive plots! </strong>This is easy to use library with a high-level interface. We can import this library by using the following code:</span></p><script src="https://gist.github.com/kk189/b0651c6d08c89cfad4177808345b11a3.js"></script><h3><span style="font-family: georgia;">4D-plot (Iris dataset)</span></h3></div><div><p class="graf graf--p" name="3037"><span style="font-family: georgia;"><script src="https://gist.github.com/kk189/e69f0a3138c6526748ff8de5c46a7ef8.js"></script>You try running this code on your own to check and interact with the plot.</span></p><p class="graf graf--p graf--empty"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Interactive Multidimensional Plot" class="graf-image" data-height="568" data-image-id="1*GwK8KRpxjopcO3CEuQePRw.png" data-width="1749" src="https://cdn-images-1.medium.com/max/1000/1*GwK8KRpxjopcO3CEuQePRw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: x-small;">(Image by Author) Interactive Multidimensional Plot</span></td></tr></tbody></table><h2><span style="font-family: georgia;">Conclusion</span></h2><p class="graf graf--p" name="3809"><span style="font-family: georgia;">I hope with this article you will be able to visualize the data using different libraries in python and start analyzing it.</span></p><p class="graf graf--p" name="ba83"><span style="font-family: georgia;">For a better understanding of these concepts, I will recommend you try writing these codes on your once. Keep exploring, and I am sure you will discover new features along the way.</span></p><p class="graf graf--p" name="7182"><span style="font-family: georgia;">If you have any questions or comments, please post them in the comment section.<span></span></span></p><!--more--><p></p><p class="graf graf--p" name="7182"><span style="font-family: georgia;">If you want to improve the way you code, check out our article:</span></p><p class="graf graf--p" name="7182"><span style="font-family: georgia;"><b><i><a href="https://patataeater.blogspot.com/2020/08/how-to-write-efficient-and-faster-code.html" target="_blank">https://patataeater.blogspot.com/2020/08/how-to-write-efficient-and-faster-code.html</a></i></b></span></p><pre class="graf graf--pre" name="e8be"><span style="font-size: x-small;">Resources:<br /><a class="markup--anchor markup--pre-anchor" data-href="https://plotly.com/" href="https://plotly.com/" rel="noopener" target="_blank">https://plotly.com/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://matplotlib.org/" href="https://matplotlib.org/" rel="noopener" target="_blank">https://matplotlib.org/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://pandas.pydata.org/" href="https://pandas.pydata.org/" rel="noopener" target="_blank">https://pandas.pydata.org/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://seaborn.pydata.org/#" href="https://seaborn.pydata.org/#" rel="noopener" target="_blank">https://seaborn.pydata.org/</a></span></pre></div></span></div><p class="graf graf--p" name="60d9"><span style="font-family: georgia;"></span></p><p class="graf graf--p" name="bcac"><span style="font-family: georgia;"></span></p></div></div></span></div></span></span></div></div><p class="graf graf--p" name="691c" style="font-family: georgia; text-align: justify;"></p></span></div>
Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com29tag:blogger.com,1999:blog-8707052728183762633.post-75015910955027491162020-08-24T11:06:00.004-07:002020-08-24T11:28:53.187-07:00 Knowing These Secrets Will Make Your Presentations Look Amazing<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="how to make amazing presentation| powerpoint icons" class="graf-image" data-height="1260" data-image-id="1*_Uvf6sh7qNbIPVxS-nPYdw.png" data-is-featured="true" data-width="2240" src="https://cdn-images-1.medium.com/max/1200/1*_Uvf6sh7qNbIPVxS-nPYdw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a>)</span></td></tr></tbody></table><h2 style="text-align: justify;"><span style="font-family: georgia;"><u>The cheat sheet to make killer presentations</u></span></h2><blockquote class="graf graf--pullquote" name="ef25" style="text-align: justify;"><span style="font-family: georgia; font-size: small;">People make bad presentations. Period.I’ve made 100’s of presentations and sat through 1000’s of them and most of them have me sleeping halfway through them or fidgeting on my phone.</span></blockquote><p class="graf graf--p" name="699e" style="text-align: justify;"><span style="font-family: georgia;">Making an impactful presentation is easier than it seems but harder than it feels.</span></p><p class="graf graf--p" name="374d" style="text-align: justify;"><span style="font-family: georgia;">With the hundreds of tools available on PowerPoint and elsewhere it is easy to get distracted and spoil the broth, but I’ll be sharing some tips I’ve learned on my way so you don’t repeat those mistakes.</span></p><p class="graf graf--p" name="cac2" style="text-align: justify;"><span style="font-family: georgia;">Before we start I want you to repeat with me:</span></p><blockquote class="graf graf--blockquote" name="e580" style="text-align: justify;"><span style="font-family: georgia; font-size: small;">Presentation tools are not Disney rides, you don’t have to ride (use) all of them.</span></blockquote><h3 style="text-align: justify;"><span style="font-family: georgia;">1. Content is King</span></h3><figure class="graf graf--figure" name="52da"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="content in shape of a crown" class="graf-image" data-height="687" data-image-id="0*_6L1IziDPZMs1jE7.jpg" data-width="960" src="https://cdn-images-1.medium.com/max/1200/0*_6L1IziDPZMs1jE7.jpg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://pixabay.com/illustrations/content-is-king-online-marketing-1132259/" href="https://pixabay.com/illustrations/content-is-king-online-marketing-1132259/" rel="noopener" style="font-family: georgia; text-align: justify;" target="_blank">Suomy</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="4b41" style="text-align: justify;"><span style="font-family: georgia;">Don’t cover up your shortcomings with fancy gimmicks.</span></p><p class="graf graf--p" name="1009" style="text-align: justify;"><span style="font-family: georgia;">This is a rookie mistake I’ve seen the most experienced individuals make.</span></p><p class="graf graf--p" name="4c44" style="text-align: justify;"><span style="font-family: georgia;">Your content will define your presentation, don’t try to trick the audience into falsely impressing them but rather work on your details.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">2. Be Consistent</span></h3><figure class="graf graf--figure" name="19f6"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="PowerPoint template" class="graf-image" data-height="1036" data-image-id="1*5Sk9v6NlbAVyqqjRQ2cMAw.jpeg" data-width="1906" src="https://cdn-images-1.medium.com/max/1200/1*5Sk9v6NlbAVyqqjRQ2cMAw.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span><br /></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="9977" style="text-align: justify;"><span style="font-family: georgia;">A variety is always good to have except when creating a presentation.</span></p><p class="graf graf--p" name="4ab3" style="text-align: justify;"><span style="font-family: georgia;">Don’t overdo the themes, don’t experiment with the colors. Commit to a particular style and let your presentation support you, not distract.</span></p><p class="graf graf--p" name="3348" style="text-align: justify;"><span style="font-family: georgia;">It can be a good idea to choose from 1000’s of pre-determined Usage-based themes that Microsoft provides and work on the different design options within them.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">3. Less Text Please</span></h3><p class="graf graf--p" name="76ee" style="text-align: justify;"><span style="font-family: georgia;">I will repeat this, a PPT is there to support you not replace you. Having boatloads of text will distract the audience from you which will ultimately result in them leaving the room confused and un-inspired.</span></p><p class="graf graf--p" name="236c" style="text-align: justify;"><span style="font-family: georgia;">In case you have text which you want to share with your audience, it is a good idea to mail it to them before or after the presentation rather than sticking up 100’s of words on a slide.</span></p><p class="graf graf--p" name="236c" style="text-align: justify;"><span style="font-family: georgia;"><b>Which one do you prefer?</b></span></p><figure class="graf graf--figure" name="0281"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="how to rephrase to make better presentation" class="graf-image" data-height="686" data-image-id="1*VdtyvfU6tXAkO2T4X5rCpg.jpeg" data-width="1280" src="https://cdn-images-1.medium.com/max/1200/1*VdtyvfU6tXAkO2T4X5rCpg.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><br /><span style="font-size: small;">(Image by <a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div></figure><h3 style="text-align: justify;"><span style="font-family: georgia;">4. Embed Videos</span></h3><figure class="graf graf--figure" name="b53b"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="embeding videos in powerpoint" class="graf-image" data-height="1046" data-image-id="1*IhF-x3Oqh57rWXyfiKkqEg.jpeg" data-width="1920" src="https://cdn-images-1.medium.com/max/1200/1*IhF-x3Oqh57rWXyfiKkqEg.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="7671" style="text-align: justify;"><span style="font-family: georgia;">Videos are an important part of any presentation, and it is equally important to present them professionally.</span></p><p class="graf graf--p" name="0a1d" style="text-align: justify;"><span style="font-family: georgia;">Many people overlook this very important feature of embedding their videos and rather carry them in different folders and the constant switching between the Presentation and Videos breaks the flow and makes you look unprofessional.</span></p><p class="graf graf--p" name="a50a" style="text-align: justify;"><span style="font-family: georgia;">You have the option of directly embedding a link or uploading a video from your device. The latter is preferred since it saves you from any connectivity issues which might arise while playing an online video.</span></p><blockquote class="graf graf--blockquote" name="30e5" style="text-align: justify;"><span style="font-family: georgia; font-size: small;"><b>Powerpoint>Insert>Video>Online Video/Video on my pc</b></span></blockquote><h3 style="text-align: justify;"><span style="font-family: georgia;">5. Don’t Go Wild With the Fonts</span></h3><p class="graf graf--p" name="7474" style="text-align: justify;"><span style="font-family: georgia;">The 100’s of fonts provided by Microsoft can look appealing and fun, but it’s not your work to deliver fun, it’s to deliver your point across, and using a variety of illegible fonts is a great way to make sure people are distracted and walk away from your presentation unimpressed.</span></p><p class="graf graf--p" name="cb3a" style="text-align: justify;"><span style="font-family: georgia;">A little bit of variety can be good to gain attention but it shouldn’t be overdone and you should try to stick with a maximum of 3 fonts and follow-through with them for the entirety of the presentation.</span></p><p class="graf graf--p" name="cb3a" style="text-align: justify;"><b style="font-family: georgia;">What font impresses you?</b></p><figure class="graf graf--figure" name="ed87"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="powerpoint fonts" class="graf-image" data-height="718" data-image-id="1*P8UFj08QIZ_Lj2v5x6Z7_A.jpeg" data-width="1152" src="https://cdn-images-1.medium.com/max/1200/1*P8UFj08QIZ_Lj2v5x6Z7_A.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><h3 style="text-align: justify;"><span style="font-family: georgia;">6. 2/4/8 Rule</span></h3><p class="graf graf--p" name="2540" style="text-align: justify;"><span style="font-family: georgia;"></span></p><blockquote><span style="font-family: georgia; font-size: small;">This can be considered as the holy grail of making presentations- I kid you not.</span></blockquote><p></p><figure class="graf graf--figure" name="d943"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="two four eight rule| 2/4/8 rule" class="graf-image" data-height="431" data-image-id="1*6qk4haOTalPw5UxuawnAVg.jpeg" data-width="1500" src="https://cdn-images-1.medium.com/max/1200/1*6qk4haOTalPw5UxuawnAVg.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="ecb2" style="text-align: justify;"><span style="font-family: georgia;">You can see how I have made sure that I follow the 2/4/8 rule, which ultimately makes it easier for the audience to understand and absorb the bulletins without being overflowed with a dump of information.</span></p><p class="graf graf--p" name="8360" style="text-align: justify;"><span style="font-family: georgia;">It also makes your work look aesthetic and well refined.</span></p><p class="graf graf--p" name="9343" style="text-align: justify;"><span style="font-family: georgia;">Mastering this rule is 50% of acing any presentation and you should work on improving this skill to gain mastery over your presentation skills.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">7. Ask Questions</span></h3><figure class="graf graf--figure" name="501c"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="how to ask questions" class="graf-image" data-height="493" data-image-id="1*AJItYDL5Bmln50fpPFOIdA.jpeg" data-width="1233" src="https://cdn-images-1.medium.com/max/1200/1*AJItYDL5Bmln50fpPFOIdA.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by</span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank"> Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="9e8d" style="text-align: justify;"><span style="font-family: georgia;">You’re not a robot and neither is your audience.</span></p><p class="graf graf--p" name="f3fc" style="text-align: justify;"><span style="font-family: georgia;">It’s a bad idea to just keep delivering your content as it is. Your bulletins should be in the form of a question and it is very optimum to have at least one question per slide.</span></p><p class="graf graf--p" name="a38d" style="text-align: justify;"><span style="font-family: georgia;">Asking questions makes it easier to have attention and keep the audience engaged while allowing for you to deliver the content as you would have.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">8. Quiz your audience</span></h3><figure class="graf graf--figure" name="51d4"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of a question mark with white background" class="graf-image" data-height="707" data-image-id="1*9ncKEliZkX09OQFKfMxYpw.jpeg" data-width="907" src="https://cdn-images-1.medium.com/max/1200/1*9ncKEliZkX09OQFKfMxYpw.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by</span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank"> Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="5ad4" style="text-align: justify;"><span style="font-family: georgia;">It is good practice to keep quizzing your audience in between the presentation to know whether you have their attention and have real-time feedback on their understanding and your delivery.</span></p><p class="graf graf--p" name="2853" style="text-align: justify;"><span style="font-family: georgia;">It also gives you an overview of what requires a deeper explanation allowing you to smartly allocate resources in real-time while making sure the audience goes back satisfied and wanting more.</span></p><p class="graf graf--p" name="82bf" style="text-align: justify;"><span style="font-family: georgia;">It is also a good idea to have real-time polls that can be achieved using software such as <a class="markup--anchor markup--p-anchor" data-href="https://www.mentimeter.com/" href="https://www.mentimeter.com/" rel="noopener" target="_blank">Mentimeter</a> <em class="markup--em markup--p-em">(this is not an affiliate link).</em></span></p><h4 class="graf graf--h4" name="f1c3" style="text-align: justify;"><span style="font-family: georgia;">9. Use Formal Language</span></h4><figure class="graf graf--figure" name="01bf"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of blah blah blah" class="graf-image" data-height="3024" data-image-id="0*ZFJFaZZ9i0LbvMeI" data-unsplash-photo-id="pIY6sz-texg" data-width="4032" src="https://cdn-images-1.medium.com/max/1200/0*ZFJFaZZ9i0LbvMeI" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@jannerboy62?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@jannerboy62?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Nick Fewings</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="72ae" style="text-align: justify;"><span style="font-family: georgia;">Formal Language is a must no matter what you are speaking about.</span></p><p class="graf graf--p" name="c969" style="text-align: justify;"><span style="font-family: georgia;">You don’t want your audience guessing what does FYI, NSFW, TMI, TY, NGL mean. It is important to connect with everyone and such slangs can create barriers.</span></p><p class="graf graf--p" name="0473" style="text-align: justify;"><span style="font-family: georgia;">You can be informal in your delivery according to the situation but make sure that you create easy to understand and formal presentations.</span></p><h4 class="graf graf--h4" name="4b8e" style="text-align: justify;"><span style="font-family: georgia;">10. Don’t Shout From Your Slides</span></h4><figure class="graf graf--figure" name="4d8d"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of a man shouting" class="graf-image" data-height="2297" data-image-id="0*dq7d7YGhuwG08f1Q" data-unsplash-photo-id="25QCezs8-oo" data-width="3451" src="https://cdn-images-1.medium.com/max/1200/0*dq7d7YGhuwG08f1Q" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@aplaceforcreation?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@aplaceforcreation?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Photo Boards</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="94cd" style="text-align: justify;"><span style="font-family: georgia;"><b>DON’T WRITE LIKE THIS.</b></span></p><p class="graf graf--p" name="be6d" style="text-align: justify;"><span style="font-family: georgia;">Uppercase Letters are the worst way of conveying a point across since it equates to virtual shouting and writing in such a way can induce negative feelings in the audience leaving them overwhelmed and leave feeling attacked.</span></p><p class="graf graf--p" name="c041" style="text-align: justify;"><span style="font-family: georgia;">It can be a good idea to use it once a blue moon but I would use it with great caution because of it’s risky nature.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">11. Use More Visuals</span></h3><figure class="graf graf--figure" name="3736"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="productcon presentation| photo of a powerpoint presentation" class="graf-image" data-height="4000" data-image-id="0*SZ3S620oLpVxBs7O" data-unsplash-photo-id="xeEukPZjZi0" data-width="6000" src="https://cdn-images-1.medium.com/max/1200/0*SZ3S620oLpVxBs7O" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@productschool?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@productschool?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Product School</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="4085" style="text-align: justify;"><span style="font-family: georgia;">Visuals are an effective way of gaining attention and conveying a message effectively.</span></p><p class="graf graf--p" name="5dd6" style="text-align: justify;"><span style="font-family: georgia;">According to research, 80% of people remember what they see compared to a measly 20% of people who remember what they have read. Using high-quality relevant visuals is a strategy that will never fail you and make sure that your audience walks away with information they’ll remember for times to come.</span></p><h4 class="graf graf--h4" name="b860" style="text-align: justify;"><span style="font-family: georgia;">12. Use GIFs</span></h4><figure class="graf graf--figure" name="0da2"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Space Origami: Make Your Own Starshade" class="graf-image" data-height="787" data-image-id="1*silfeOpgchDOrD1mo21EUA.gif" data-width="1400" src="https://cdn-images-1.medium.com/max/1200/1*silfeOpgchDOrD1mo21EUA.gif" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://www.jpl.nasa.gov/edu/learn/project/space-origami-make-your-own-starshade/" href="https://www.jpl.nasa.gov/edu/learn/project/space-origami-make-your-own-starshade/" rel="noopener" style="font-family: georgia; text-align: justify;" target="_blank">NASA</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="b2e8" style="text-align: justify;"><span style="font-family: georgia;">GIFs are a great way to demonstrate a process and make sure your presentation is fun to look at.</span></p><p class="graf graf--p" name="8b17" style="text-align: justify;"><span style="font-family: georgia;">GIFs make sure that a point is delivered in a bite-sized visual without showing long videos each time you have to display a process. It also gives you the flexibility of breaking down steps at a binary level and having visuals to explain as you proceed with your presentation.<span></span></span></p><a name='more'></a><p></p><h3 style="text-align: justify;"><span style="font-family: georgia;">13. Secret Tip: Designer Ideas</span></h3><figure class="graf graf--figure" name="c754"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Design ideas in powerpoint" class="graf-image" data-height="915" data-image-id="1*sBjGYgQ1t1r8nqihtDX3mA.jpeg" data-width="1918" src="https://cdn-images-1.medium.com/max/1200/1*sBjGYgQ1t1r8nqihtDX3mA.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="0248" style="text-align: justify;"><span style="font-family: georgia;">Designer ideas is a cool feature by Microsoft which allows you to customize every single slide with a plethora of slide and content specific suggestions from Microsoft.</span></p><p class="graf graf--p" name="a82b" style="text-align: justify;"><span style="font-family: georgia;">It makes your presentation aesthetically appealing by providing various templates, icons, etc while making it super easy to customize your slides.</span></p><blockquote class="graf graf--blockquote" name="8849" style="text-align: justify;"><em class="markup--em markup--blockquote-em"><span style="font-family: georgia; font-size: small;">Powerpoint>Design>Designer</span></em></blockquote><h3 style="text-align: justify;"><span style="font-family: georgia;">14. Canva is a GameChanger</span></h3><figure class="graf graf--figure" name="5d0f"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="making a presentation in canva" class="graf-image" data-height="928" data-image-id="1*a7G6oLOgfzN6w33Or9LmBA.jpeg" data-width="1914" src="https://cdn-images-1.medium.com/max/1200/1*a7G6oLOgfzN6w33Or9LmBA.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="7a5b" style="text-align: justify;"><span style="font-family: georgia;">Various online resources provide templates but Canva is a different league. You can create anything that you can think of: presentations, logos, calendars, visiting cards, etc.</span></p><p class="graf graf--p" name="5b60" style="text-align: justify;"><span style="font-family: georgia;">It replaces the need for hardcore design knowledge.</span></p><p class="graf graf--p" name="e89b" style="text-align: justify;"><span style="font-family: georgia;">It has so many free templates to make cool presentations and most of their designs are topic-specific so you always have templates to choose from no matter the occasion.</span></p><p class="graf graf--p" name="2441" style="text-align: justify;"><span style="font-family: georgia;">Not many people know about it, so using it already takes you to the top of the chain.</span></p><p class="graf graf--p" name="19a5" style="text-align: justify;"><span style="font-family: georgia;">You can check out canva at <a class="markup--anchor markup--p-anchor" data-href="https://www.canva.com/join/jpz-vyx-qwp" href="https://www.canva.com/join/jpz-vyx-qwp" rel="nofollow noopener noopener noopener" target="_blank">https://www.canva.com/join/jpz-vyx-qwp</a> (this is my referral link)</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">15. Have an Assistant</span></h3><figure class="graf graf--figure" name="a468"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of a man giving a presentation" class="graf-image" data-height="2681" data-image-id="0*k1IZ_F-_j_DMtJbB" data-unsplash-photo-id="rxpThOwuVgE" data-width="4767" src="https://cdn-images-1.medium.com/max/1200/0*k1IZ_F-_j_DMtJbB" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@austindistel?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@austindistel?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Austin Distel</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="7971" style="text-align: justify;"><span style="font-family: georgia;"><b>What’s worse than a bad presentation?</b></span></p><p class="graf graf--p" name="2cce" style="text-align: justify;"><span style="font-family: georgia;"><i>A distracted Presenter.</i></span></p><p class="graf graf--p" name="d0be" style="text-align: justify;"><span style="font-family: georgia;">It is not a good sight to see the presenter continuously turn back and bend to toggle between slides. You can ask someone to assist in you moving between slides or use a remote if that is available.</span></p><h4 class="graf graf--h4" name="5bdc" style="text-align: justify;"><span style="font-family: georgia;">16. Carry the Original Device</span></h4><figure class="graf graf--figure" name="acfe"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="error | error 404" class="graf-image" data-height="2800" data-image-id="0*j9oujScYx9rmf4Rd" data-unsplash-photo-id="JpTY4gUviJM" data-width="4000" src="https://cdn-images-1.medium.com/max/1200/0*j9oujScYx9rmf4Rd" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@visuals?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@visuals?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">visuals</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="ecdf" style="text-align: justify;"><span style="font-family: georgia;">Please carry the device in which you created the presentation.</span></p><p class="graf graf--p" name="497d" style="text-align: justify;"><span style="font-family: georgia;">I cannot count the number of times I’ve witnessed presenters being embarrassed because the Presentation just won’t run due to- incompatibility. It is better to carry your device rather than risking incompatibility on a new device or a different version of the windows.</span></p><p class="graf graf--p" name="939e" style="text-align: justify;"><span style="font-family: georgia;">It is also preferred to carry your presentation in multiple formats such as- JPEG, PDF, etc.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">17. Use the Slide Show</span></h3><figure class="graf graf--figure" name="742e"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="slide show in powerpoint" class="graf-image" data-height="720" data-image-id="1*aY7dSpA_CWfBVbAmyysVaQ.jpeg" data-width="1280" src="https://cdn-images-1.medium.com/max/1200/1*aY7dSpA_CWfBVbAmyysVaQ.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="c58b" style="text-align: justify;"><span style="font-family: georgia;">Using the presenter’s view/slide show looks professional and aesthetic as compared to toggling between all the slides on the screen. You can also rehearse your timings, record your presentation, and pre-define aspects of how the presentation will be presented.</span></p><p class="graf graf--p" name="56e0" style="text-align: justify;"><span style="font-family: georgia;">You can also use <strong class="markup--strong markup--p-strong">Ctrl+H</strong> to hide the cursor on the presenter’s view and use <strong class="markup--strong markup--p-strong">A key</strong> to bring the cursor back.</span></p><p class="graf graf--p" name="3d5e" style="text-align: justify;"><span style="font-family: georgia;"><strong class="markup--strong markup--p-strong">Ctrl+P</strong> can be used to bring up a pen tool during the slide show allowing you to draw during your presentations.</span></p><blockquote class="graf graf--blockquote" name="c26d" style="text-align: justify;"><span style="font-family: georgia; font-size: small;"><b>Powerpoint>Slideshow>From Beginning</b></span></blockquote><h3 style="text-align: justify;"><span style="font-family: georgia;">18. Avoid Overwhelming Transitions</span></h3><figure class="graf graf--figure" name="0e94"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of an arrow" class="graf-image" data-height="2532" data-image-id="0*MMubNqc3Im39ag6e" data-unsplash-photo-id="MAgPyHRO0AA" data-width="3782" src="https://cdn-images-1.medium.com/max/1200/0*MMubNqc3Im39ag6e" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@helloimnik?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@helloimnik?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Hello I’m Nik 🎞</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="cdcf" style="text-align: justify;"><span style="font-family: georgia;">Avoid Using Any Comical Animations.</span></p><p class="graf graf--p" name="7876" style="text-align: justify;"><span style="font-family: georgia;">Don’t reveal your text letter by letter, don’t have the text fly-in, don’t have that comical audio, don’t have those transitions.</span></p><p class="graf graf--p" name="adb9" style="text-align: justify;"><span style="font-family: georgia;">Powerpoint presentation is not Disney movies and you don’t need to show your animation skills because all that makes you look is a fool who has just learned how t use PowerPoint.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">19. Practice, Practice, Practice</span></h3><figure class="graf graf--figure" name="b47c"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of practice with scrabble blocks" class="graf-image" data-height="3888" data-image-id="0*Kv9YEQlk87uMrzZl" data-unsplash-photo-id="Lt9xJf6QUxo" data-width="5184" src="https://cdn-images-1.medium.com/max/1200/0*Kv9YEQlk87uMrzZl" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@brett_jordan?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@brett_jordan?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Brett Jordan</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="dc0e" style="text-align: justify;"><span style="font-family: georgia;">It is very important to practice until you can give a fantastic presentation even if you did not have your Presentation on you.</span></p><p class="graf graf--p" name="d77d" style="text-align: justify;"><span style="font-family: georgia;">Speaking out your presentation in front of the mirror and timing it is a good way to be in better control on the D-day. It also allows you to work upon your presentation and experiment with what works and what doesn’t.<span></span></span></p><!--more--><p></p><h3 style="text-align: justify;"><span style="font-family: georgia;">20. Don’t Go Wild With the Colors</span></h3><figure class="graf graf--figure" name="c806"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of hello world" class="graf-image" data-height="1079" data-image-id="1*mpXuNWcyaw-XU509uD1KSQ.jpeg" data-width="1920" src="https://cdn-images-1.medium.com/max/1200/1*mpXuNWcyaw-XU509uD1KSQ.jpeg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">(Image by </span><a class="markup--anchor markup--figure-anchor" data-href="https://medium.com/@beginningofthefuture" href="https://medium.com/@beginningofthefuture" style="font-family: georgia; text-align: justify;" target="_blank">Author</a><span style="font-family: georgia; text-align: justify;">)</span></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><p class="graf graf--p" name="7d0b" style="text-align: justify;"><span style="font-family: georgia;">It is a strict no when it comes to using dark background colors.</span></p><p class="graf graf--p" name="709c" style="text-align: justify;"><span style="font-family: georgia;">Many people use colors on PowerPoint-like they’re using paint. I always result in bad looking unprofessional presentation</span></p><p class="graf graf--p" name="6d40" style="text-align: justify;"><span style="font-family: georgia;">Dark background colors with either contrasting or dark font color are always a bad idea owing to the stress dark colors create on our eyes and the difficulty it creates for the farther seated audience to read the Presentation.</span></p><p class="graf graf--p" name="6d40" style="text-align: justify;"><span style="font-family: georgia;">It is a good practice to stick with light colors and preferably White.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">21. Be Thoughtful of the Font Size</span></h3><figure class="graf graf--figure" name="6517"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="photo of font size" class="graf-image" data-height="4155" data-image-id="0*tvWO24H85qUElt9J" data-unsplash-photo-id="zw07kVDaHPw" data-width="6225" src="https://cdn-images-1.medium.com/max/1200/0*tvWO24H85qUElt9J" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><span style="font-family: georgia; text-align: justify;">Photo by </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com/@alex_andrews?utm_source=medium&utm_medium=referral" href="https://unsplash.com/@alex_andrews?utm_source=medium&utm_medium=referral" rel="photo-creator noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Alexander Andrews</a><span style="font-family: georgia; text-align: justify;"> on </span><a class="markup--anchor markup--figure-anchor" data-href="https://unsplash.com?utm_source=medium&utm_medium=referral" href="https://unsplash.com/?utm_source=medium&utm_medium=referral" rel="photo-source noopener noopener" style="font-family: georgia; text-align: justify;" target="_blank">Unsplash</a></span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia; font-size: small;"></span></div><figcaption class="imageCaption" style="text-align: justify;"><br /></figcaption></figure><blockquote class="graf graf--blockquote" name="e157" style="text-align: justify;"><span style="font-family: georgia; font-size: small;">A good presentation with an illegible font size is a bad presentation.</span></blockquote><p class="graf graf--p" name="acf6" style="text-align: justify;"><span style="font-family: georgia;">It is a good idea to be thoughtful of the farthest seating audience and keeping the <strong class="markup--strong markup--p-strong">text size </strong>above <strong class="markup--strong markup--p-strong">30</strong> and the <strong class="markup--strong markup--p-strong">title size</strong> above <strong class="markup--strong markup--p-strong">40</strong>.</span></p><p class="graf graf--p" name="28ba" style="text-align: justify;"><span style="font-family: georgia;">The art of presenting is like every other skill, it takes practice, experience, and a lot of mistakes to figure out what works and how to deliver the best presentations.</span></p><p class="graf graf--p" name="bd7a" style="text-align: justify;"><span style="font-family: georgia;">These tips are what I have learned from my experience and I hope they help you in avoiding mistakes I’ve experienced.</span></p><p class="graf graf--p" name="b85c" style="text-align: justify;"><span style="font-family: georgia;">Every skill is an individual journey to be experienced and I wish you all the best for your presentations.</span></p><div class="graf graf--mixtapeEmbed" name="1144"><span style="font-family: georgia;"><a class="js-mixtapeImage mixtapeImage u-ignoreBlock" data-media-id="9516c2b5dc4d06a51c171bd23eef74b2" data-thumbnail-img-id="0*UisuFLtOG0CLJk39" href="https://towardsdatascience.com/beginners-guide-to-dynamic-programming-8eff07195667" style="background-image: url("https://cdn-images-1.medium.com/fit/c/240/240/0%2aUisuFLtOG0CLJk39");"></a></span></div><p class="graf graf--p" name="637a" style="text-align: justify;"><span style="font-family: georgia;">Please feel free to reach out in case of questions, suggestions, tips.</span></p>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com15tag:blogger.com,1999:blog-8707052728183762633.post-49277505701832987532020-08-21T11:24:00.001-07:002020-08-21T12:07:40.533-07:00Everything You Need to Know About Google Foobar Challenge<div style="text-align: left;"><div style="text-align: justify;"><img alt="Google Foobar Challenge for Hiring" src="https://cdn-images-1.medium.com/max/1000/1*Jd1hEk67rUrOf_3pmwddiw.gif" /></div><div style="text-align: justify;"><span style="font-family: georgia;">Recently, while searching a keyword “headless chrome” on Google I got an unusual pop-up on my window, with a message:</span></div><div style="font-family: georgia; text-align: center;"><b>"Curious developers are known to seek interesting problems. Solve one from Google?"</b></div><div style="font-family: georgia; text-align: justify;">I was surprised to see Google sending me a challenge to solve and I accepted it immediately! Clicking on “I want to play” landed me on <a class="markup--anchor markup--p-anchor" data-href="http://foobar.withgoogle.com/" href="http://foobar.withgoogle.com/" rel="noopener" target="_blank">Google’s Foobar</a> page.</div><div style="font-family: georgia; text-align: justify;"><br /></div><strong class="markup--strong markup--blockquote-strong" style="font-family: georgia;"><div style="text-align: center;"><strong class="markup--strong markup--blockquote-strong">It was Google Foobar Challenge!</strong></div><div style="text-align: justify;"><strong class="markup--strong markup--blockquote-strong"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="font-family: "times new roman"; margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Google Foobar Challenge for Hiring" src="https://cdn-images-1.medium.com/max/2600/1*0uI-54fiwZzrL-1CRdM8xw.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;"><br /></span><br /></td></tr></tbody></table></strong><h2>What exactly is Google Foobar Challenge?</h2></div></strong><div style="font-family: georgia; text-align: justify;">Google Foobar challenge is a secret hiring process by the company to <strong class="markup--strong markup--p-strong">recruit top programmers</strong> and <strong class="markup--strong markup--p-strong">developers</strong> around the world. And it is known that several developers at Google are hired by this process.</div><div style="font-family: georgia; text-align: justify;"><br /></div><div style="font-family: georgia; text-align: justify;">The challenge consists of<strong class="markup--strong markup--p-strong"> five levels</strong> with a total of <strong class="markup--strong markup--p-strong">nine questions</strong>, with the level of difficulty increasing at each level.</div><h2 style="font-family: georgia; text-align: justify;">What to do after getting the challenge?</h2><div style="text-align: justify;"><span style="font-family: georgia;">After selecting “I want to play” option you land on Foobar’s website which has a Unix-like shell interface, including some standard Unix commands like </span><code class="markup--code markup--p-code"><span style="font-family: courier;"><b>help, cd, ls, cat and etcetera</b></span><span style="font-family: georgia;">.</span></code></div><div style="text-align: justify;"><code class="markup--code markup--p-code"><figure class="graf graf--figure graf--layoutOutsetCenter" name="2410"><img alt="Google Foobar Challenge for Hiring" class="graf-image" data-height="956" data-image-id="1*Ig5mNt0VLxFykej9Zni8Og.png" data-is-featured="true" data-width="1918" src="https://cdn-images-1.medium.com/max/1500/1*Ig5mNt0VLxFykej9Zni8Og.png" /><figcaption class="imageCaption"><br /></figcaption><figcaption class="imageCaption"><p class="graf graf--p" name="7843"><span style="font-family: georgia;">So, the challenge begins with a sci-fi adventure storyline (in the blue text above). To begin the time-limited challenge we have to enter a <code class="markup--code markup--p-code">request</code> command in the shell.</span></p><div><span style="font-family: georgia;">After requesting the challenge, four files are added in the command-line folder: </span><span style="font-family: courier;"><b><code class="markup--code markup--p-code">solution.java</code> , <code class="markup--code markup--p-code">solution.py</code> , <code class="markup--code markup--p-code">readme.txt</code></b></span><span style="font-family: georgia;"> and </span><code class="markup--code markup--p-code"><b><span style="font-family: courier;">constraints.txt</span></b> </code><span style="font-family: georgia;">which we have to access using </span><code class="markup--code markup--p-code" style="font-family: georgia;">cat and ls</code><span style="font-family: georgia;"> command (listed in </span><code class="markup--code markup--p-code" style="font-family: georgia;">help</code><span style="font-family: georgia;">).</span></div><div style="text-align: center;"><b><span style="font-family: georgia;">We have a choice to solve the question either in </span><span style="font-family: courier;">java or python2.7 .</span></b></div><div><span style="font-family: georgia;">To start writing the code, we have to run the command </span><span style="font-family: courier;"><b><code class="markup--code markup--p-code">edit file.py</code> or <code class="markup--code markup--p-code">edit file.java</code></b></span><span style="font-family: georgia;"> and a code editor will open-up on the same webpage (and </span><code class="markup--code markup--p-code" style="font-family: georgia;">save</code><span style="font-family: georgia;"> the code using the short-cut keys). We can verify our code at any time by running the </span><code class="markup--code markup--p-code"><b><span style="font-family: courier;">verify file</span></b></code><span style="font-family: georgia;"> command. The code will be verified by running on several test cases of which two are visible and rest are hidden test cases. Once the code has passed all the test cases accurately, we can submit our solution to that question running the </span><span style="font-family: courier;"><code class="markup--code markup--p-code"><b>submit file</b></code><b> </b></span><span style="font-family: georgia;">command.</span></div><figure class="graf graf--figure" name="cda4"><span style="font-family: georgia;"><img alt="Google Foobar Challenge for Hiring" class="graf-image" data-height="1080" data-image-id="1*ltTOOp0ULXH8gvdnXCXflA.png" data-width="1920" src="https://cdn-images-1.medium.com/max/1000/1*ltTOOp0ULXH8gvdnXCXflA.png" /></span><figcaption class="imageCaption"><span style="font-family: georgia;"><br /></span></figcaption></figure><div><h2><span style="font-family: georgia;">Five levels of foobar challenge</span></h2><span style="font-family: georgia;">The level of difficulty keeps on increasing as progress further in the challenge.</span></div><div><span style="font-family: georgia;"><br /><strong class="markup--strong markup--p-strong">Level 1: </strong></span></div><div><span style="font-family: georgia;">This level has only one question, which was pretty straightforward and easy to solve. It does not require any special algorithm to solve it. Forty-eight hours are given to solve this question.</span></div><div><span style="font-family: georgia;"><br /><strong class="markup--strong markup--p-strong">Level 2: </strong></span></div><div><span style="font-family: georgia;">There are two questions at this stage with the time of seventy-two hours to solve each question. Both the questions were based on the basic principles of linear algebra and mathematics.</span></div><div><span style="font-family: georgia;"><br /><div style="text-align: center;"><b>After solving both the questions at this level, we get a referral link i.e. we can invite our one friend to take Google Foobar Challenge!</b></div></span></div><div><span style="font-family: georgia;"><br /><strong class="markup--strong markup--p-strong">Level 3: </strong></span></div><div><span style="font-family: georgia;">This is where the challenge starts to get a little <strong class="markup--strong markup--p-strong">tricky</strong>. To pass this level, we have to solve three questions with a time of seven days for each question.</span></div><div><span style="font-family: georgia;"><br />To solve these questions, it required a good knowledge of mathematics and programming concepts like dynamic programming, Markov’s chaining, etc.</span></div><div><span style="font-family: georgia;"><br />But you don’t need to worry if you don’t know these concepts, you can always learn these concepts online. Challenge gives you enough time to understand the concept and implement them in the given programming problem. In one of the questions, I solved the problem with the most intuitive method but it was not optimal enough for large values and required implementation of <strong class="markup--strong markup--p-strong">dynamic programming</strong> to get the result.</span></div><div style="text-align: center;"><span style="font-family: georgia;"><b>After completing level 3, we are asked to fill-out our details for being contacted by a Google recruiter!</b></span></div><div><p class="graf graf--p" name="12da"><span style="font-family: georgia;">They ask your basic information: name, phone number, email address, country, CV (optional), and if you are a student or a professional.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Google Foobar Challenge for Hiring recruitment form" class="graf-image" data-height="202" data-image-id="1*T5K5eJxAjGlYAOAsK3CbSQ.png" data-width="1696" src="https://cdn-images-1.medium.com/max/1500/1*T5K5eJxAjGlYAOAsK3CbSQ.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: georgia; font-size: small;">Foobar Recruitment Form</span><br /></td></tr></tbody></table><p class="graf graf--p" name="5418"><span style="font-family: georgia;"><strong class="markup--strong markup--p-strong"><br />Level 4:</strong></span></p><p class="graf graf--p" name="5418"><span style="font-family: georgia;">I found this level the most difficult of all the five. It required the implementation of several concepts to solve a single problem. There are a total of two questions at this level and time of a total of two weeks is given to solve each question. </span></p><p class="graf graf--p" name="5418" style="text-align: center;"><span style="font-family: georgia;"><b>Extensive knowledge of algorithms and data structures is required at this level.</b></span></p><p class="graf graf--p" name="75ac"><span style="font-family: georgia;">The first question was based on the concept of number theory and graphs. I had to implement the Bellman-Ford algorithm in order to solve this question.</span></p><p class="graf graf--p" name="51c4"><span style="font-family: georgia;">It took me a lot of time to understand these concepts and implement them to solve these questions. But I was able to solve both of these questions on time.</span></p><p class="graf graf--p" name="51c4" style="text-align: center;"><span style="font-family: georgia;"><b>After successfully completing level 4 you get another referral link to invite your one more friend to try this challenge!</b></span></p><p class="graf graf--p" name="56ec"><span style="font-family: georgia;"><strong class="markup--strong markup--p-strong">Level 5:</strong></span></p><p class="graf graf--p" name="56ec"><span style="font-family: georgia;">This was the second hardest problem of the whole challenge and was based on a purely mathematical concept. The final level only had a single question and <strong class="markup--strong markup--p-strong">twenty-two days</strong> were given to solve that problem!</span></p><p class="graf graf--p" name="75e1"><span style="font-family: georgia;">The problem required understanding of permutations and combinations and implementation of the <a class="markup--anchor markup--p-anchor" data-href="https://en.wikipedia.org/wiki/P%C3%B3lya_enumeration_theorem" href="https://en.wikipedia.org/wiki/P%C3%B3lya_enumeration_theorem" rel="noopener" target="_blank"><strong class="markup--strong markup--p-strong">Pólya enumeration theorem</strong></a><strong class="markup--strong markup--p-strong"> </strong>and <a class="markup--anchor markup--p-anchor" data-href="https://en.wikipedia.org/wiki/Burnside%27s_lemma" href="https://en.wikipedia.org/wiki/Burnside%27s_lemma" rel="noopener" target="_blank"><strong class="markup--strong markup--p-strong">Burnside’s lemma</strong></a>. After understanding these two theorems coding part was pretty much simple.</span></p><p class="graf graf--p" name="75e1" style="text-align: center;"><span style="font-family: georgia;"><b>With the submission of this question, the Google Foobar Challenge is completed!</b></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Google Foobar Challenge for Hiring" class="graf-image" data-height="732" data-image-id="1*3xL22mkcxkf1AgqU9Gb9ng.png" data-width="1920" src="https://cdn-images-1.medium.com/max/1000/1*3xL22mkcxkf1AgqU9Gb9ng.png" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: justify;"> </span><strong class="markup--strong markup--figure-strong" style="text-align: justify;"><span style="font-family: georgia; font-size: small;">Foobar Challenge Completed</span></strong></td></tr></tbody></table><p class="graf graf--p" name="bd00"><br /></p><div><span style="font-family: georgia;">After ending the challenge, I got an encrypted string which was easy to decrypt using</span><span style="font-family: courier;"><b> </b><b><code class="markup--code markup--p-code">base64</code>.</b></span></div><div><span style="font-family: courier;"><b><br /></b></span></div><div><div><span style="font-family: courier; font-size: small;">import base64</span></div><div><span style="font-family: courier; font-size: small;">encrypted="THE ENCRYPTED MESSAGE"</span></div><div><span style="font-family: courier; font-size: small;">my_eyes=str.encode("MY USER NAME")</span></div><div><span style="font-family: courier; font-size: small;">decoded=base64.b64decode(encrypted)</span></div><div><span style="font-family: courier; font-size: small;">decrypted=""</span></div><div><span style="font-family: courier; font-size: small;">for i in range(0,len(decoded)):</span></div><div><span style="font-family: courier; font-size: small;"> decrypted+=chr((my_eyes[i%len(my_eyes)] ^ decoded[i]))</span></div><div><span style="font-family: courier; font-size: small;">print(decrypted)</span></div><div style="font-family: georgia;"><br /></div><div><span style="font-family: georgia;">This was the code I used to decrypt the message. The decrypted message was:</span></div><div><span style="font-family: georgia;"><br /></span></div><div><span style="font-family: courier;"><b>{'success':'great','colleague':'esteemed','efforts':'incredible','achievement':'unlocked','rabbits':'safe','foo':'win!'}</b></span></div><h2 style="font-family: georgia;">What will happen after completing the challenge?</h2><div style="font-family: georgia;">After the successful completion of all the five levels, chances are that you will be contacted by Google's recruiter for an interview.</div><div style="font-family: georgia; text-align: center;"><b>You may receive an email or a phone call and if you cracked the interview then you can be hired at Google.</b></div><h2 style="font-family: georgia;">How to get Foobar Challenge?</h2><div style="font-family: georgia;">Unfortunately, this challenge is not available to everyone and Google sends it only to specific developers (it may be based on their search history - technical keywords).</div><div style="font-family: georgia;"><br /></div><div style="font-family: georgia;">Don't worry if you didn't get this invite yet, this is not the only way to get a job at Google.</div><div style="font-family: georgia; text-align: center;"><b>Don't find Foobar, let Foobar find you!</b></div><div style="font-family: georgia; text-align: center;"><img alt="Google Foobar Challenge for Hiring" src="https://cdn-images-1.medium.com/max/1000/1*g56cabJFM71ckAHNQZPRvQ.png" /></div><h2 style="font-family: georgia;">Conclusion</h2><div style="font-family: georgia;">I would say this is a great opportunity to learn and I would recommend you to solve the questions if you get an invitation.</div><div style="font-family: georgia;">While solving the problems don't keep your aim to get hired at Google but to learn the new techniques and experience one of the best coding challenges.</div><div style="font-family: georgia;">Foobar is more about learning and implementing, instead of knowing everything before!</div><div style="font-family: georgia;"><p class="graf graf--p" name="477c">To improve your code check out our <a class="markup--anchor markup--p-anchor" data-href="https://patataeater.blogspot.com/2020/08/how-to-write-efficient-and-faster-code.html" href="https://patataeater.blogspot.com/2020/08/how-to-write-efficient-and-faster-code.html" rel="noopener" target="_blank">article here</a>.</p></div><div style="font-family: georgia;">If you have any queries or comments, please post them in the comment section.</div></div><div><pre class="graf graf--pre" name="9589"><span style="font-family: georgia; font-size: small;">Resources:<br /><a class="markup--anchor markup--pre-anchor" data-href="https://foobar.withgoogle.com/" href="https://foobar.withgoogle.com/" rel="noopener" target="_blank">https://foobar.withgoogle.com/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://www.freecodecamp.org/news/the-foobar-challenge-googles-hidden-test-for-developers-ed8027c1184/" href="https://www.freecodecamp.org/news/the-foobar-challenge-googles-hidden-test-for-developers-ed8027c1184/" rel="noopener" target="_blank">https://www.freecodecamp.org/news/the-foobar-challenge-googles-hidden-test-for-developers-ed8027c1184/</a></span></pre></div><p></p></div></figcaption></figure></code></div></div>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com16tag:blogger.com,1999:blog-8707052728183762633.post-2362767733974833712020-08-18T07:14:00.003-07:002020-08-19T07:19:43.876-07:009 Techniques to Write Your Code Efficiently<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="efficient and faster code" src="https://images.unsplash.com/photo-1523800503107-5bc3ba2a6f81?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1000&q=80" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;">(Photo by <a href="https://unsplash.com/@oskaryil?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" rel="nofollow" target="_blank">Oskar Yildiz</a> on <a href="https://unsplash.com/s/photos/programming?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" rel="nofollow" target="_blank">Unsplash</a>)</span></td></tr></tbody></table><div style="text-align: justify;"><span style="font-family: georgia;">It’s really easy to write<strong class="markup--strong markup--p-strong"> efficient</strong> and <strong class="markup--strong markup--p-strong">faster code</strong>. Efficient code, not just only improves the functionality of the code but it can also reduce the time and space complexity of the programming.</span></div><div style="text-align: justify;"><span style="font-family: georgia;"><br /></span><span style="font-family: georgia;">Speed is one of the major factors in deciding the<strong class="markup--strong markup--p-strong"> quality of the code</strong>, for instance, your code might be producing the required result but it takes some time to execute then it will not be considered a quality code. An alternative approach to the same problem producing faster results will be considered better.</span></div><div style="text-align: justify;"><span style="font-family: georgia;"><br /></span><span style="font-family: georgia;">The code should be clean i.e. <strong class="markup--strong markup--p-strong">comprehensible </strong>and <strong class="markup--strong markup--p-strong">readable</strong> so that it can be reused (saving the efforts of rewriting the whole program from scratch), adding new features, and making the process of debugging more easier.</span></div><div style="text-align: justify;"><span style="font-family: georgia;"><br /></span><span style="font-family: georgia;">In this article, I will cover some<strong class="markup--strong markup--p-strong"> simple tips </strong>and <strong class="markup--strong markup--p-strong">techniques</strong> which we can easily apply to make our code more elegant and efficient.<br /><div style="text-align: center;"><b>"There is always more than one method to solve the problem."</b></div></span><h2><span style="font-family: georgia;">How to write code efficiently</span></h2><span style="font-family: georgia;">Now let me show you how we can make our code efficient and faster.</span></div><div style="text-align: justify;"><div><h3><span style="font-family: georgia;">1. Creating function</span></h3><span style="font-family: georgia;">If your block of code is being repeated or it has a similar structure and it can be parameterized, generalize it by creating a function for the same.<br />Creating a function will be computationally cheaper and will create a much cleaner (readable) code as well.</span></div><div><pre class="graf graf--pre" name="1347"><img alt="efficient and faster code" src="data:image/png;base64,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" /></pre><div><span style="font-family: georgia;">Rather than writing the same code again and again, now we will just have to call the function to execute the code block.</span></div></div><div><h3><span style="font-family: georgia;">2. Eliminate unessential operations</span></h3><p class="graf graf--p" name="d4b9"><span style="font-family: georgia;">Processing of the same data, again and again, can be avoided by saving the required value in variables (for example- calculating the length of string, conversion of integer to string, etcetera). The data can be stored in a dictionary or an array for faster execution of code (elimination of recalculation of values). For example:</span></p><pre class="graf graf--pre" name="20c1"><img alt="efficient and faster code" src="data:image/png;base64,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" /></pre><div><span style="font-family: georgia;">In the code above, the length of </span><code class="markup--code markup--p-code"><b><span style="font-family: courier;">l</span></b></code><span style="font-family: georgia;"> will be calculated 100 times because of the</span><span style="font-family: courier;"><b> </b><code class="markup--code markup--p-code"><b>for</b></code></span><span style="font-family: georgia;"> loop. This unnecessary calculation can be eliminated by storing the value in a variable.</span></div><pre class="graf graf--pre" name="50b3"><img alt="efficient and faster code" src="data:image/png;base64,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" /></pre><p class="graf graf--p" name="0318"><span style="font-family: georgia;">Now, as we can see the calculation of length multiple times is eliminated. This method can be very effective for large codes.</span></p><div><h3><span style="font-family: georgia;">3. Avoid declaring unnecessary variables</span></h3><div><span style="font-family: georgia;">In a function, if we want to return a certain value, instead of storing it in a variable we should directly return it. For example:<br /></span><img alt="efficient and faster code" src="data:image/png;base64,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" /><span style="font-family: georgia;"><br />Instead of writing the above-mentioned code this way, we can write it as:<br /></span><img alt="efficient and faster code" src="data:image/png;base64,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" /><span style="font-family: georgia;"><br />This simple technique can make our code look cleaner.<br /></span><h3><span style="font-family: georgia;">4. Use appropriate algorithms</span></h3><span style="font-family: georgia;">Selecting the correct algorithm for solving the programming problem is very necessary. Only an efficient coding style won’t make your code efficient and fast. Think of an algorithm that works with low time and space complexity even for large values.<span><a name='more'></a></span></span></div></div><div><div><h3><span style="font-family: georgia;">5. Learn the concept of dynamic programming</span></h3><span style="font-family: georgia;">Dynamic programming is an approach that can be applied to a certain class of problems (the problems which require the calculation of certain values multiple times, resulting in high time complexity of the code). This technique significantly optimizes the code and make it much more efficient.<br />Here the concept is to <b>break the problem into sub-problems</b> and <b>save the result</b> (store the values in a list/array or dictionary) for the future so that we will not have to compute that same problem again.</span></div></div><div><h3><span style="font-family: georgia; font-size: x-large;">6. Minimize the use of If-Else</span></h3><div><span style="font-family: georgia;">If-else approach is one of the most common and easy approaches to solving code-branching problems.</span></div><p class="graf graf--p" name="8b03"><b><span style="font-family: georgia;">However, it results in less readable code and increases the complexity with any new conditional requirement implemented using If-Else.</span></b></p><p class="graf graf--p" name="8c4b"><span style="font-family: georgia;">The appropriate strategy should be applied to obtain an efficient code. Few of them are:</span></p><ul class="postList"><li class="graf graf--li" name="1454"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong">Eliminate unnecessary else block-</strong> Understand this with the example.</span></li></ul><pre class="graf graf--pre" name="b307"><img alt="efficient and faster code" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA3gAAACTCAYAAADcBbuSAAAdq0lEQVR4Ae3dz4scVQIH8P1L+qQrqIdlg87gYVnWgHjKxIOLHsa4C8mQ054UR+aSQBTXCciyt7FDWHKIt0QwghcvChEED4On+V/eUr+6X1VXVVd3V01P93wOw8x019SP9z7vTX3rvar+w2g0Cr6UAQMMMMAAAwwwwAADDDCw+Qb+oBI3vxLVoTpkgAEGGGCAAQYYYICBxICAZwTTCC4DDDDAAAMMMMAAAwxsiQEBb0sq0hUbV2wYYIABBhhggAEGGGBAwBPwXK1hgAEGGGCAAQYYYICBLTEg4G1JRbpa42oNAwwwwAADDDDAAAMMCHgCnqs1DDDAAAMMMMAAAwwwsCUGBLwtqUhXa1ytYYABBhhggAEGGGCAAQFPwHO1hgEGGGCAAQYYYIABBrbEgIC3JRXpao2rNQwwwAADDDDAAAMMMCDgCXiu1jDAAAMMMMAAAwwwwMCWGBDwtqQiXa1xtYYBBhhggAEGGGCAAQYEPAHP1RoGGGCAAQYYYIABBhjYEgMC3pZUpKs1rtYwwAADDDDAAAMMMMCAgCfguVrDAAMMMMAAAwwwwAADW2JAwDvXivwgvPbl7+Hdw7srNaBXD38P755EX6us7/N/h7Ozb6Kvf4d751omK1xlKfb910/Ch+e+z3fCk9PT8ORuZf/vPgmnp0/CnXPfn8p+2P5KbczVT54YYIABBhhgYFMNLBzwbt68Gcbj8eTrs88+Cy+99NJSJ1OHh4eT9STrTH7f1ILstt+rB7ws3P0SXtvtodHlAem3//3tQpX7ve+/CWddQtvaAt5+ePDzaTj9+UHYnwlSbe/1UGcz27PObm1POSknBhhggAEGGLgcBpYKeP/5z3/Cn/70p95CQRIQk6B48QLeTji4n4XZ41s7PRzvqgHvbvhLMnK3yohdFBDSIHV28UbsOge86FjOs8O68/R0zihdNrp3+vROD2YuR0d0nvVnW0wxwAADDDDAwDYbEPAaQsLOreMwHh+Hg+sH4Xg8Dhcp4L1984NegsNFDVIXdb/SjuDGg/C8bmpm1VE6VbNmCmd1Ob/3YnmbO2nH5iSEAQYYYIABBhYxIODVnWBfPwrj+wdhJ3lvd7WAN3O/XO0IXD4yN7mv7tvwat1+jbLlziXg/fOT8NvZN+HZ5+UGVQ1fH/7v63w65T/Cs+hevvppn38Lj36N7/f7Ojz6Z77+fHvl+wGjZeMpm8XUzGJ73/+jMSSk+1csl3yvLLvY/o9COnpXOzWzXE6jUZepmvfCs7OzcPbrozXcQ1jdX78v0nFalhcGGGCAAQYYuKgGBLzaIBWBXTrg5dMxv3wYXpxso2aK5u7D8PbJ7yEObVkoLEJe/jeT8Bc9XCV9bbH78WYCTxx+zr4Jk2C2SMBL1xGFtTyAlcJhEeDioDb6W3j0/ewDUqohsrnx5IGxEtqy5YswGU9BzUNotA/T8piz/2kdZlMvn3+93xgo433d//p5OD19Hh7ciDxNLIzC6PNn4SwJeGe/TYNu/L6fO5VzXOZ+brDGEksMMMAAAwxcGgMC3jzsywa8vW/DuyfV8DUb8NIwVwqByQla00hd0+vLndS1BqkFA14pzI2yIDUJi6NRaN1WpQ66L9sS8NKQGYW2YhuV8FkEvHn7nwaHdNplS2ArtlF8nzud0wieQLZc21Vuyo0BBhhggAEGmgwIeMXJeNP3JQNefXCrBrymwFZdrgDctHzx/mLfW4PUQgEvHiVL9qEa8Kq/t+9n636V6qk54KXrqH2ATHlfsoA3b/+z/c1G5Bb5CITFRvyaGqnX270oH+XDAAMMMMAAAwxMDQh4pcAwLZgJkiEDXj49s/SZdvFUzJmnZW5owGsIi5MyrtRBbwEvmoo53VYW8Ip78QS8GvOV+piWnWWVBQMMMMAAAwwwcNENCHjzTmaHDHiNUzGbGs6GBryZEb2m48teP4+AV0wfFfDa6+Kid2D2T/0xwAADDDDAAANlAwLeQAHvxZu/hHdPigel5IWe3pcXf45d01TMciVN0a474OWjX9HIWLeAlE+ljP5uekyzx1q/ztnlRslDWpKnctY9ZKX1HrzpvXn12ypP45zsq3vwLs3NyZM6n9c/eJ8JBhhggAEGGLhgBgS8eRWy5AjeqPp0zPT3X8LbX8YBbxRGeeiLn6LZfHJ5jgEvH3Wbhqc83CVPzIyCWueAlD/cZLq+JLDVP0VzlC9bjLI1l0dLwCvCX7Svxb2B8T503v/UyWL31HmKZl0o91qzZ2WjbBhggAEGGGBgdQMCXl3Ay0PdeDwOs1/H4WC3Y8EXI3bpfXXZaF768JXqvXW19+JVn8CZbPM8A94ojIqPNsg/SiF50mQaiKLQtFBAqqzv7Gw6klZtzNl66z8HL3uASvRe9FEP5adh5gEwer8aGhfa/9ESn4P39E7LVS1P0azWu9879i11/ZbXWtqactW2GGCAAQYujwEBz0mRk6JFDOQffTD3s/AWnc65yD5YllkGGGCAAQYYYICBBgMCXkPBuMpxea5yLFrXd56etn+A+SibynnaOnqnfBctd8szwwADDDDAAAMMzDcg4Al4rn4sbGA/PPg5CXl1n4mXv/fzg7C/8HrnN1idmjJigAEGGGCAAQYYaDMg4DkJF/CWMpCN0j25W+lg0qmZdcGvstxS27SOts7Me3wwwAADDDDAAAOjsFTAix888tlnn4WXXnppqZBweHhYeohJ8rtK0TAZYIABBhhggAEGGGCAgeUMLBzwFPRyBa3clBsDDDDAAAMMMMAAAwwMbUDAM1XOqCkDDDDAAAMMMMAAAwxsiQEBb4CK/Otf/xr++Mc/aiQDlO3QVzys31U1BhhggAEGGGCAgU02IOANEEIEPJ3CJncK9p1fBhhggAEGGGBgcw0IeAKekcYBDOgUN7dTVHfqjgEGGGCAAQY22YCAN8DJvRE8ncImdwr2nV8GGGCAAQYYYGBzDQh4GxbwDm//GH6692P44f2/t4+8Xfkq/HAvWzZZ/qd734X/XtlcqDoZdccAAwwwwAADDDDAwHwDAt42Brwi3N3+uD0EDnDsGt38RqeMlBEDDDDAAAMMMMDAUAYEvKaQs3sQjsfj6IPYj8PBbjeI656i+d77313yEbsPwmtf/h7ePWn5Orwr/DbZ9zobDDDAAAMMMMDAxhoQ8OrwpuGuHOj2Pk3CXvm1ptR9MQLew3BYd2yX8bW9b8O7J7+E1zoG9KZ69Xq3CxzKSTkxwAADDDDAAAPrMyDgdQ08+Yje8a2duWl+iIBX3HuX3U/3Y3j0VjOabASvj4C3Hx78fBpOT6dfz7/en3v8F65BtwW86L1XD+MRv2/Dq5GN9L0vH4YXo9dGuw/D2ye/h7/slevixZu/lEYP3775wUpl9uH/fgtnZ2fh2efl7Vy4co7Lxs8r1bm6ZZ0BBhhggAEGljUg4HU9EV1zwJtUcH5/3UzAe+th+vCVIgDOfF/ifrw7T5+HBzeixnX3SRr2Ni7kRSFuUo5Fvafv5cFuMm0zn+IZBbquAS8LiVE4zEPg8iHvw/Do17M04J19f09oKOrNdxYYYIABBhhggIFaAwJeVxjXj9L78Y6uR4Gn4W+HGMGbBJOmgBftS38jeNVjzUf0fn4Q9qPtTfat6bU8GMYjgfHPT+5m29n/+nlptDBe5vS0EjabtlX3eoeAVw1g2SjcNKh1CnitI3rTdc0tr8oxGMGrOvT7ooYszwwDDDDAAAOXx4CAVzmZrse/F47SB64chb0Oy29vwBuFO09Pw+miAa9DmdWXe08NsUPAq06zrO5Pl4BXDYWTdbRtf91lY/u1V74mdad8lA8DDDDAAAMMbJgBAW9uhRXhrtsDVpITw60JeDcehOfR/XeTETUBL+voKiN22fTM+D6++GcPeRGaerpgMbfPsh3WGGCAAQYYuMwGBLzWk6WdcHA/+6iELlMzC0hbEfCKcPf0TumqzVIjeBswRXPQEbxWYzrgot34zgIDDDDAAAMMMLC6AQGv8eR7uXCXoNyGgJfdDzd739tSAa+xjFcH3KkTaJsimb43+yTM6nrrpmgWT8uchMO27axQBu7BOycnK9RR1Yvf1RkDDDDAAAMMrMuAgFd7Urd8uEsqchsC3mjmiZnRRyZcwimaWZibTrMswl3yYeqTgDcqPmB9+QeqzHYEnqI5Wyb+YSgTBhhggAEGGGCgyYCAVxfw8idmjtMHq2RTNKc/z78Xr/+A93F4dO/Hho9BmP28u76eoll9qmXytMvLOoKXNKDSPXbpRyjcDX8pBbysoyktd1L9CIbFOyMjeIuXWVOH53VlyQADDDDAAAPbbkDAqwt4K77Wf8DTELe9ITo+xhlggAEGGGCAAQb6MCDgrRjm6ipBwNM461x4jQsGGGCAAQYYYICBoQ0IeAJe6SmZQ4Ozfp0aAwwwwAADDDDAAAPDGRDwBDwBbwADOq3hOi1lq2wZYIABBhhggIFmAwLeACf3pmg2g9MYlQ0DDDDAAAMMMMAAA8MZEPAEPCN4AxjQaQ3XaSlbZcsAAwwwwAADDDQbEPAGOLk3gtcMTmNUNgwwwAADDDDAAAMMDGdAwLvgAU9YHA6/jkXZMsAAAwwwwAADDGybAQFPwDNFcwAD29ZROB7//BhggAEGGGCAgc0wIOANcHLf56hbn+vSKDejUaon9cQAAwwwwAADDDCwrAEBT8AzgjeAgWUbpL/TmTPAAAMMMMAAAwysYkDAG+Dkvs9Rtz7XtQqU7G9fCG+eXAu3v3hlraHw9S+uhdsnV8LLHesuXf7xtbD/0Qtr3e/Vy19npwwZYIABBhhggAEG2g0IeB1DwiKQ+gxlfa5rkWOoX/Z8At68ADfv/eq+C3jtnUC1vPyuvBhggAEGGGCAgc01IODVBry9cDQeh3Hp6zgc7Har6D5DWZ/rKjXUq1fC/uOr4c2r2TG9/NHVDiNzmxnwSsddW9/d6tV6lBMDDDDAAAMMMMDARTcg4HU84d/7NAl83UJen6Gsz3WVMKYB743wen786SjX3KmXAl6pDDva8Tf+ETDAAAMMMMAAAwyclwEBr+tJ+u5BOB6Pw9H1+Tj7DGV9rquE6sYb0X1sWXCru0etmN54+/G1MPmaCYKvhHfi96ORwdI228o6DZzRNkrrK99zN5miWfmbd26U66a679X3p/vWw/63HZv33PvIAAMMMMAAAwwwcE4GBLyuBb0tAS8JdtXwVPk9C0L5aF3pYSY1I3j5+uJwmE73fHwtNAeqchCbBq3s9UmAa6ibaXCrTDF9PB2RLK0zD4L1+5OHu1JofSW8Uzru9v0tbathny2jDBlggAEGGGCAAQbOw4CA1+mEfCcc3B+H8f2DsNNh+T5H3fpcVwyqfM9dFnJKASgNbtMAlf1tNeBVfy8abV04LN6b/71bwKvsW1uIW/a9DnUdl6mf59etMlJGDDDAAAMMMMDAsAYEvMaT+DzUTR60chT2GpctV1KfoazPdcWNKQ1RxahVGoDKgak+ZFUCXR6c4tG7YhvZKF7DiNqccqzf9rSMa99fOsRNp2eWAu6cfSyO0/dpvSgLZcEAAwwwwAADDKzfgIDX9UT++lH6VM3jWztz5w/3Gcr6XFfc4JKQNAlmKwa8umCUBbxyaIy33/ZzbYCL6qn2/aUDXtII8+A6maq63H63HZP31t/ZqQN1wAADDDDAAAOXwYCAFwWHeRWePkmzwzTNPkNZn+uaDTI1DzXJ7z2rDVFFECqN/NXfa7c5I3iVji4PireXeVDMApbmWfN+pV6U7dwLS8wwwwADDDDAAAOJAQFvgRPHzQ94BfryPXdpGKs8VKQ2oBUPaCkCXjXwTcqyMpVz8nqx/fbvtduO1lEbPlcawavsT9u6ov3QiVbKTdkIYQwwwAADDDDAwNoNCHgdEe7cOk6naG7HxyQkAW86DTENVJPQlp+05yGnOo1z/+Ra+QPR89AXT9PMAtp0/QsHoZonc8br6DXg3XhjOlU1t7Dy/nc0FR+Tn4VFBhhggAEGGGCAgT4MCHh1J+P5RyKMJw9Y6f4EzaRS+pxW2ee6JmDS8DYNYGlgqga8pFyKEbv03rTsgSm1y5aWS6Z9Lvdwlcn+jUYhC1nRFNJohLFbwJs+PGX2YyHK+zezrR72Pz4WP+usGWCAAQYYYIABBs7LgIBXF/BWfK3PUNbnus4Lle3owBhggAEGGGCAAQYYWI8BAW/FMFcHt89Q1ue66vbVa+tpeMpduTPAAAMMMMAAAwwMYUDAE/DWfiPoELCtU4fJAAMMMMAAAwwwcBkNCHgCnoA3gIHL2Jk4Zv9EGWCAAQYYYICB9RsQ8AY4ue9zWmWf69Lg1t/g1IE6YIABBhhggAEGGBjSgIAn4BnBG8DAkI3Wuv1TYIABBhhggAEGGGgyIOANcHLf56hbn+tqQuB1HQQDDDDAAAMMMMAAA9thQMC74AFPQ9uOhqYe1SMDDDDAAAMMMMDAeRgQ8AQ8UzQHMHAejdc2/JNggAEGGGCAAQYYqBoQ8AY4uTetUkOrNjS/M8EAAwwwwAADDDBwHgYEPAHPCN4ABs6j8dqGfxIMMMAAAwwwwAADVQMC3gAn92sbwbvyVfjh3o/hp+Lrk6/CewMcXxWR33UsDDDAAAMMMMAAAwxcDAMC3gABaG0BLzqWw9s/hp8EPKOTkQmd7sXodNWDemCAAQYYYICBIQ0IeB1OgPc+HYfxeBzG9w/CToflBTyNdshGa918McAAAwwwwAADDDQZEPDmBbbrR1m4E/CMhs2z4n1GGGCAAQYYYIABBtZsQMBrrYCdcHB/HMaf7oV0FG/dI3hvPZzeX5fcZ3f748YGNHeKZvV+vXsPw2FNWbz3/nelbf7w/t8btnkvPDs7C2e/Pgof1qyn6QqD1119YoABBhhggAEGGGCgPwMCXksY2bl1HMbjo7A3Gq094GVB67vw3ytF5X8cHrWEvNaAl4e7UlhLXqsExnQdcfCr+7ui/D5/Fs6SgHf2W3j0z2IffddZMcAAAwwwwAADDDBwngYEvCKgVL/vHoTj8TgcXc9ArncELwtzpUCW7G86oheHvmnjaQ14LX83wZeHuUdvTdeZvJcFzbrRPiN4k7KrWvJ7w6hv2ZbyUx4MMMAAAwwwwMDqBgS8hpPvaqCr/t6Gr/eHrDQFsoYQluxba8DL/+6ne/XhMPn7xiDXtC8N5dhWTt5bvQErQ2XIAAMMMMAAAwwwEBsQ8OqCSfpgleNwsDvFss6AV70PbvI5d/nn3VVH2ZIKbg146THnUzyLz8yLp2IWfz95L/psvfS15mAY4/Lz1I+yUBYMMMAAAwwwwAAD52FAwJsJeHvhaDwOx7d2StPK1hnw2qZiNiGZH/CiBpaOyiUhbjr1snEEb6a8ovV4r2SmqW68zgwDDDDAAAMMMMDAUAYEvGooiT8WIflohLqvOU/T7H2KZstUzCYYCwW8pAyqUy+rv1fLaeZ39+A11YXXdeAMMMAAAwwwwAAD52VAwJsJKvX41jqCN5ky2X1qZFvAS0bnqtM60+WjEbzR6O/hv5+UR/VaUXqKptG7jm2p1ZF1cMQAAwwwwAADDKxkQMDrCGjdAS85Ka69F++Tr8J7+THUvp/fR1d9AmcW6KJ766L1xCfgM8sl66t8nEK2vBG8uNz8XH+hRLkoFwYYYIABBhhgYFgDAl7HgLcIxN6naA6wj4scj2WHbYTKV/kywAADDDDAAAMM9GVAwBsgPAl4GmhfDdR6WGKAAQYYYIABBhhYxICAJ+CtNMd3EWyW1TkxwAADDDDAAAMMMDCsAQFPwBPwBjCg4xq241K+ypcBBhhggAEGGKg3IOANcHJvimY9No1QuTDAAAMMMMAAAwwwMKwBAU/AM4I3gAEd17Adl/JVvgwwwAADDDDAQL0BAW+Ak/s+R/D+9a9/hT//+c9C2AD1pFOo7xSUi3JhgAEGGGCAAQY214CAN0BwEPA2t0HozNQdAwwwwAADDDDAwCYbEPAEPKODAxjY5E7BvvunxgADDDDAAAMMbK4BAW+Ak3sjeJvbIHRm6o4BBhhggAEGGGBgkw0IeALeAiN4L4Q3T66F21+8ssDf9N9BvP7FtXD75Ep4uWPdpcs/vhb2P3phrfu9yR2Ffe/fsTJVpgwwwAADDDAwhAEBr2NIWKTwt3cE73wC3rwAN+/9al0JeDrPqgm/M8EAAwwwwAAD22pAwKsNeHvhaDwO45qvo+vzG8NGBLyrV8L+46vhzavZ8bz80dUOI3ObGfC2tfE6rvltURkpIwYYYIABBhi4bAYEvJaAd3xrZ6kpfZsT8N4Ir+fHn45yzZ16KeBdtg7C8fqnyAADDDDAAAMMbJYBAe+yBrwbb0T3sWXBre4etWJ64+3H18LkayYIvhLeid+PRgY7dwjpiGK0jdL6yvfcTaZoVv7mnRvlxlfd9+r7033rYf9rHZX3Z7o9rysLBhhggAEGGGCAgWEMCHi1J+bZFM2tHMFLgl01PFV+z4JQPlpXephJzQhevr44HKbTPR9fC82Bqh3zJMDV1s0oTINbZYrp4+mIZKnDyINg/f7k4a4UWl8J75SOu31/S9tq2GfLKEMGGGCAAQYYYICB8zAg4NWekNffg9fl/ruk0jZhimb5nrss5JQCUBrcpgEqw1gNeNXfi0ZbFw6L9+Z/7xbwKvvWFuKWfa/Wxvz9P4+GaxvqgQEGGGCAAQYYYKDOgIDX8SR+59Zx+tCVLqN6mxDw0hBVjFqlAagcmOpDViXQ5cEpHr0rkGWjeA0janPKvH7b0wZc+/7SIW46PbMUcOfsY3Gcvk/rRVkoCwYYYIABBhhgYP0GBLzOJ/I74eD+OIzvH4SdOX+zKQFvEsxWDHh1wSgLeOXQ2LXB1wa4qMxr31864CWNMA+uk6mqy+131+Oz3Po7PnWgDhhggAEGGGBgWw0IeFFwmFfJe58mH51wFPbm/M3FDXjVIFPzUJP83rPaEFUEodLIX/29dpszglfp3PKgeHuZB8XMcTHPl/crdaE8l3qKL0ccMcAAAwwwcLkNCHidTyLz+/I+3Zt70nVxA16BvXzPXRrGKg8VqQ1oxQNaioBXDXyTsqxM5Zy8Xmy//XvttqN11IbPlUbwKvvTtq5oP3SelXJTNnP7BmaYYYABBhhggIGhDQh4nU5K8+mZHUbvkgrbjIA3nYaYBqpJaMsbXR5yqtM490+ulT8QPQ998TTNLKBN178w4ponc8br6DXg3XgjTI4xt7Dy/ncypXOL69TPPDDAAAMMMMAAA/0YEPDqTsZ3D8LxOJmOGX11GLkrUF74gJeGt2kASwNTNeAl5VKM2KX3pmUPTKldtrRcMu1zuYerFOWXfM9CVjSFNBph7Bbwpg9Pmf1YiPL+zWyrh/2Pj8XP/XRWylE5MsAAAwwwwAAD8w0IeHUBb8XXLnzAW/H4NKz5DUsZKSMGGGCAAQYYYICBdRgQ8AYIOwKexryOxmyb3DHAAAMMMMAAAwwIeAKeB0MMYEDnqnNlgAEGGGCAAQYYWIcBAW+Ak3sjeBrzOhqzbXLHAAMMMMAAAwwwIOAJeEbwBjCgc9W5MsAAAwwwwAADDKzDgIA3wMm9ETyNeR2N2Ta5Y4ABBhhggAEGGBDwBDwjeAMY0LnqXBlggAEGGGCAAQbWYUDAc3Iv4DHAAAMMMMAAAwwwwMCWGBDwtqQi13F1wDZdlWKAAQYYYIABBhhg4GIZEPAEPFdrGGCAAQYYYIABBhhgYEsMCHhbUpGunFysKyfqQ30wwAADDDDAAAMMrMOAgCfguVrDAAMMMMAAAwwwwAADW2JAwNuSilzH1QHbdFWKAQYYYIABBhhggIGLZUDAE/BcrWGAAQYYYIABBhhggIEtMSDgbUlFunJysa6cqA/1wQADDDDAAAMMMLAOAwKegOdqDQMMMMAAAwwwwAADDGyJAQFvSypyHVcHbNNVKQYYYIABBhhggAEGLpYBAU/Ac7WGAQYYYIABBhhggAEGtsSAgLclFenKycW6cqI+1AcDDDDAAAMMMMDAOgwIeAKeqzUMMMAAAwwwwAADDDCwJQb+sLu7G3wpAwYYYIABBhhggAEGGGBg8w0IeAKugM8AAwwwwAADDDDAAANbYkDA25KKdLVl86+2qEN1yAADDDDAAAMMMLCqAQFPwHO1hgEGGGCAAQYYYIABBrbEwP8B+pgZFrafancAAAAASUVORK5CYII=" /></pre><p class="graf graf--p" name="2e46"><span style="font-family: georgia;">Now remove else block from this code.</span></p><pre class="graf graf--pre" name="36d6"><img alt="efficient and faster code" src="data:image/png;base64,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" /></pre><p class="graf graf--p" name="d9a5"><span style="font-family: georgia;">This code will work the same as the one above but here the else block is eliminated.</span></p><ul class="postList"><li class="graf graf--li" name="3cdc"><span style="font-family: georgia;"><strong class="markup--strong markup--li-strong">Storing If-Else in a dictionary-</strong> This method can be used in the case where we have to perform an operation based on some condition and some more operations will be added later.</span></li></ul><pre class="graf graf--pre" name="08c8"><img alt="efficient and faster code" src="data:image/png;base64,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" /></pre><p class="graf graf--p" name="619a"><span style="font-family: georgia;">Here, if we want to perform another operation then we will have to add another</span><b><span style="font-family: courier;"> <code class="markup--code markup--p-code">elif</code></span></b><span style="font-family: georgia;">(else-if)! This does not look like a nice approach. Now let’s try the same problem by using a dictionary.</span></p><pre class="graf graf--pre" name="87fb"><img alt="efficient and faster code" src="data:image/png;base64,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" /></pre><pre class="graf graf--pre" name="87fb" style="text-align: center;"><span style="font-family: georgia;"><b>This method eliminates the need for the If-Else statement!</b></span></pre><p class="graf graf--p" name="7d67"><span style="font-family: georgia;">In the above code,</span><b><span style="font-family: courier;"> <code class="markup--code markup--p-code">if</code></span></b><span style="font-family: georgia;"> and </span><code class="markup--code markup--p-code"><b><span style="font-family: courier;">elif</span></b></code><span style="font-family: georgia;"> (else-if) conditions are stored in the dictionary and the value of </span><code class="markup--code markup--p-code" style="font-family: georgia;">variable_two</code><span style="font-family: georgia;"> are easily obtained using</span><span style="font-family: courier;"><b> </b><code class="markup--code markup--p-code"><b>.get()</b></code></span><span style="font-family: georgia;"> (where the second parameter of</span><span style="font-family: courier;"><b> </b><code class="markup--code markup--p-code"><b>.get()</b></code><b> </b></span><span style="font-family: georgia;">the function acts as else statement).</span></p><p class="graf graf--p" name="100c"><span style="font-family: georgia;">This approach makes the code look cleaner and easily readable.</span></p><div><h3><span style="font-family: georgia;">7. Break the loops when necessary</span></h3><span style="font-family: georgia;">The number of iterations of the loops should be reduced as much as possible. The loop should break when the required value is calculated instead of running all the iterations. For example:<br /></span><img alt="efficient and faster code" src="data:image/png;base64,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" /><span style="font-family: georgia;"><br />As we can see in the above example, instead of running over all the iterations, the loop ends immediately after the required result is obtained. This will make the code efficient and computationally cheaper to execute.</span></div></div><div><h3><span style="font-family: georgia; font-size: x-large;">8. Avoid declaring variables in the global scope</span></h3><span style="font-family: georgia;">When you have to write a very large code (thousands or millions of lines), avoid creating variables and loops in the global scope, as they can create noise and influence code desirable.</span></div><div><span style="font-family: georgia;"><br /><b>We should write code inside functions or methods. And these functions and methods should be defined inside the class definitions.</b></span></div><div><span style="font-family: georgia;"><b><br /></b>This will make code clean and easy to maintain. Moreover, we should always add comments wherever necessary to make the code easily understandable and reusable for later use.<br /></span><h3><span style="font-family: georgia;">9. Keep practicing!</span></h3><span style="font-family: georgia;">Somethings can only be learned by practicing. So just keep coding, you will definitely learn many things through experience that no one will ever teach you.<br /></span><h2><span style="font-family: georgia;">Conclusion:</span></h2><span style="font-family: georgia;">These are some essential tips and techniques for any developer who wants to achieve mastery in their field. Implementing these basic rules at the right time can make your code much better and professional at the same time.<br />So now it’s the time to put an end to old practices and try executing new techniques to make your code efficient, readable, and maintainable to work with. </span></div></div><div><span style="font-family: georgia;"><br /></span></div></div>
<iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="480" src="https://www.youtube.com/embed/vfqvzgkYYF0" width="853"></iframe><div><pre class="graf graf--pre" name="8ac7" style="text-align: justify;"><span style="font-family: georgia; font-size: small;">Resources:<br /><a class="markup--anchor markup--pre-anchor" data-href="http://creative-punch.net/2014/02/3-tips-to-write-faster-and-more-efficient-code/" href="http://creative-punch.net/2014/02/3-tips-to-write-faster-and-more-efficient-code/" rel="nofollow" target="_blank">http://creative-punch.net/2014/02/3-tips-to-write-faster-and-more-efficient-code/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://docs.oracle.com/cd/E80738_01/pt854pbh2/eng/pt/tpcd/task_WritingMoreEfficientCode-0749ba.html#topofpage" href="https://docs.oracle.com/cd/E80738_01/pt854pbh2/eng/pt/tpcd/task_WritingMoreEfficientCode-0749ba.html#topofpage" rel="noopener" target="_blank">https://docs.oracle.com/cd/E80738_01/pt854pbh2/eng/pt/tpcd/task_WritingMoreEfficientCode-0749ba.html#topofpage</a></span></pre></div>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com29tag:blogger.com,1999:blog-8707052728183762633.post-39511836307320812722020-08-14T10:05:00.003-07:002020-08-15T10:28:31.611-07:00Robots can Perform Daily Tasks<div class="separator"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="robot performing real world tasks" src="https://images.unsplash.com/photo-1525338078858-d762b5e32f2c?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1000&q=80" style="margin-left: auto; margin-right: auto;" title="Robot by Lukas" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Robot by<a href="https://unsplash.com/photos/hND1OG3q67k" target="_blank"> Lukas</a><br /></td></tr></tbody></table><p style="margin-left: 1em; margin-right: 1em;"> </p></div><p><b></b></p><blockquote><b><span style="font-family: georgia;"> <span style="background-color: white; color: #333333; white-space: pre-wrap;">Is this the ending or the beginning?</span></span></b></blockquote><b><span style="font-family: georgia;"><span style="background-color: white; color: #333333; white-space: pre-wrap;"></span></span></b><p></p><span id="docs-internal-guid-1b7ef5c3-7fff-7762-f64a-1da3aea4329b"><div style="text-align: justify;"><span style="font-family: georgia;"><br /></span></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Robots are the future and they will replace and recreate anything that comes in their way.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">As the world continues to evolve, so do the ways robots interact with the world. It does feel like a nice idea if our robot could help us in the day-to-day activities while at the same time adapt to different surroundings.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The present world does have some robots which can perform chores but I want you to envision a robot that would respond to your commands and help you in multiple chores without complaining.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">To help us out in such a way robots must perceive their surroundings as we humans do. A clear mental image of the environment around them is a necessity for the robots to perceive the world as it is.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The particular function of perceiving their surroundings is particularly a hard one for robots where the pixels have to be transformed into a real-life understanding of the world.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">What has been proposed?</span></span></h2><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">A new model called- </span><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">3D Dynamic Scene Graphs is being developed which gives robots the ability to quickly generate a 3D map of its surroundings including objects with semantic labels (for example, a chair vs a table) along with people, rooms, walls and other structures in the surroundings.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The model also helps the robot to extract information from the 3D map, allowing us to query the location of objects and rooms along with the movement of people in its path.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The compressed representation of the surroundings helps the robot to make quick decisions and plan its path. This is somewhat resembling humans who plan their path from Point A to Point B by thinking about the streets and landmarks rather than every single position.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">How does the technology work?</span></span></h2><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">At present, robotic vision and navigation have forayed along two routes; real-time 3D mapping enabling robots to reconstruct their environment and semantic segmentation which lets robots classify features as semantic objects mainly done on 2D images.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The new model of spatial perception works on both real-time 3D imagining of the surroundings while labeling objects in the surrounding environment.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Kimera- an open-source library is the key component of this model which was developed to construct a 3D environment while attempting to label whether an object is a chair or desk. In short, kimera is a mix of 3D imaging and semantic tagging.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">To generate a 3D mesh kimera uses an existing neural network which has been trained on millions of real-world images, to accurately predict the label of each pixel projecting the label in 3D using a technique known as ray-casting used for real-time rendering of animations in computers.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This ultimately results in a color-coded 3D map of the robot’s environment.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This technique alone would be computationally expensive and time-consuming. </span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">To encounter this problem researcher’s built off kimera algorithms to construct 3D scene graphs form kimera’s initial map.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">A scene graph is a general data structure commonly used by vector-based graphics editing applications and modern computer games, which arranges the logical and often spatial representation of a graphical scene.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">In this particular case of the scene graphs, the algorithm breaks down kimera’s 3D mesh into distant semantic layers so that the robot can see through a scene of a particular layer. This layered representation saves the bot from h</span></span><span style="background-color: white; color: #222222; font-family: georgia; white-space: pre-wrap;">aving to make sense of billions of points and faces.</span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="559" src="https://www.youtube.com/embed/SWbofjhyPzI" width="994"></iframe></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">What does this mean for the future?</span></span></h2><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"> The technology called spatial AI is in its infancy but promises to bring along with itself exciting innovations in fields such as self-driving cars, rescue missions, manufacturing, and domestic robots.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"> It can also be used in wearables such as AR goggles which can be asked questions such as “Where is the nearest exit”, “Where did I leave my car keys?” etcetera.</span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Our take on this</span></span></h2><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Technology is always exciting and so is the thought of having human-like robots that will be able to perform multiple tasks for us. We are living in an era which is witnessing the revolution of the world technologically and it is only times when humans achieve a feat to move over to the next. It is exciting to look at the prospective future whilst scaring at the same time to imagine what humans will look and behave like in the few years. </span></span></p><span style="font-family: georgia;"><div style="text-align: justify;"><br /></div></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">There is no way to predict the future than to create it and live through it.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;">Bibliography:</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;"><a href="https://roboticsconference.org/program/papers/79/" target="_blank">https://roboticsconference.org/program/papers/79/</a></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; color: #333333; font-size: small; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><a href="https://www.sciencedaily.com/releases/2020/07/200715131222.htm" target="_blank">https://www.sciencedaily.com/releases/2020/07/200715131222.htm</a></span></p></span>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com1tag:blogger.com,1999:blog-8707052728183762633.post-66853003468651685462020-08-13T10:11:00.001-07:002020-08-25T00:27:03.576-07:00Teleport Across the World with AI<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Holographic AR AI Holoportl Machine" src="https://images.squarespace-cdn.com/content/v1/5d018e3a16fb890001e312b9/1584761050143-52MER4C6IX5KM95FWIOT/ke17ZwdGBToddI8pDm48kP8N5KLxKqzW7xoy-OuA-ht7gQa3H78H3Y0txjaiv_0fDoOvxcdMmMKkDsyUqMSsMWxHk725yiiHCCLfrh8O1z5QPOohDIaIeljMHgDF5CVlOqpeNLcJ80NK65_fV7S1UYAswtmfg2fJ6dgr7UAPeBhZtCnBfXt7-uVnY3X5mdIxtfYk0dVZayboTAeqjnjomg/cid4AE1DFB2-73D7-40B1-9640-D0B6ED89CB54.jpg?format=1500w" style="margin-left: auto; margin-right: auto; text-align: left;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;">(</span><a href="https://portlhologram.com/" style="font-size: small;" target="_blank">Image Source</a><span style="font-size: small;">) </span><b style="font-size: small;">Holoportl Machine</b></td></tr></tbody></table><p style="text-align: justify;"><i><b><span style="font-family: georgia;">"<span style="text-align: justify; white-space: pre-wrap;">If you can't BE there, BEAM there!"</span></span></b></i></p><span id="docs-internal-guid-4f71172b-7fff-09c5-2d17-dc5df1876469"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">“Visit” your friends anywhere in the world just by sitting in your room with this new AI-powered holographic machine launched by PORTL (a Los Angeles-based technology company). </span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">With this <u><b>new way of communication</b></u>, you can “HoloPort” to wherever you want as realistic 3D holograms. Holoportl is a booth-sized machine that will beam the life-sized hologram in real-time allowing the users to interact with anyone anywhere in the world. This machine also allows the user to interact with recorded holograms of historical figures or deceased relatives (using StoryFile to collaborate on the historical figures, animations, etc. for the machine)!</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Features and Technology </span></span></h2><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Holographic AR AI Holoportl Machine" src="https://images.squarespace-cdn.com/content/v1/5d018e3a16fb890001e312b9/1581442186425-G0KIU99033W3B3U82EJB/ke17ZwdGBToddI8pDm48kAn6g6DK9zV4C3Ja0S7k2ENZw-zPPgdn4jUwVcJE1ZvWQUxwkmyExglNqGp0IvTJZamWLI2zvYWH8K3-s_4yszcp2ryTI0HqTOaaUohrI8PI3uNQ5IEdZIP-3iRjNaKc8HkykXDpQfq5Y_elcpVeIxM/PORTL-3-RENDER-W-NOTES+AND+DIMS.PNG?format=1000w" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;">(</span><a href="https://portlhologram.com/" style="font-size: small;" target="_blank">Image Source</a><span style="font-size: small;">) </span><b style="font-size: small;">Holoportl Machine<br /><br /></b></td></tr></tbody></table><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Holoportl is 7 feet tall, 5 feet wide and 2 feet deep AI-powered device which will give the person being projected, “a unique ability to see, hear, and fully interact with their global audiences in 4K holographic resolution!”. </span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The machine will come with an awesome feature- “<u>motion capture</u>” that will detect the motion (with the help of the camera) if someone walks in front of the screen. Moreover, AI gives the device the power of facial recognition giving the user a more personalized experience while interacting with the hologram.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The device will include stereo speakers on right and left side to give a realistic overall experience. It will also have 18,000 lumens of light evenly distributed with curved inner corners for better projection (with adjustable brightness).</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Along with all these features, the user can record and play all the holographic animations!</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><b>Process of Holoportation-</b> Any person who has a white background and a camera can send their hologram to the machine. These are the two basic requirements for using the device. </span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This is an easily accessible touch screen device that you just need to plug in the socket to activate. This device will be available in the price range of $60,000 to $85,000 depending on the features.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">How Holoportl Can be Used?</span></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Holoportl can be used in various fields</span></span></p><ul style="margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The device can be used in museums to have interactive historical holograms, holograms of animals, etc.</span></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This can be used for advertising purposes as well, like holo-fashion shows, generating foot traffic, interacting with the audience, and many more ways.</span></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The education sector will also be able to benefit from this technology, professors from any university can “beam” at multiple locations at the same time.</span></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Holoportl can work as an information guide at tourist places and airports.</span></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Live concerts and performances of singers, comedians, etcetera can be beamed at different locations at once.</span></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This device can replace the cardboard standees in theater lobbies with movie star holograms.</span></span></p></li></ul><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Uses of this machine are not limited only to the above mentioned, but this device can be used in many more different ways which the user can explore!</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Our Take on This </span></span></h2><p class="graf graf--p" name="1cd2"><span style="font-family: georgia;">While teleportation is a dream far from reality, it is exciting to see what limits we can push with the evolving technologies and constantly trying to “humanize" technology. The high price and complex usage requirements could be met with resistance from a price-sensitive audience and there is no way we will be seeing this in private use in the near future.</span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="506" src="https://www.youtube.com/embed/CbQYqSM-Gwo" width="900"></iframe></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-size: small;">Bibliography</span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><a href="https://portlhologram.com/" target="_blank"><span style="font-size: small;">https://portlhologram.com/</span></a></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><a href="https://nerdist.com/article/hologram-machine-portl/?utm_medium=social&utm_source=twitter.com&utm_campaign=social+flow" target="_blank"><span style="font-size: small;">https://nerdist.com/article/hologram-machine-portl/?utm_medium=social&utm_source=twitter.com&utm_campaign=social+flow</span></a></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><a href="https://www.zdnet.com/article/own-your-own-hologram-device-with-the-portl-hologram-machine/" target="_blank"><span style="font-size: small;">https://www.zdnet.com/article/own-your-own-hologram-device-with-the-portl-hologram-machine/</span></a></p></span>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com7tag:blogger.com,1999:blog-8707052728183762633.post-36413840767716234632020-08-07T04:29:00.000-07:002020-08-07T04:29:31.124-07:00What are Classification and Regression in ML?<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Machine Learning" src="https://images.unsplash.com/photo-1496942299866-9e7ab403e614?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1000&q=80" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;">(Photo by <a href="https://unsplash.com/@skraidantisdrambliukas?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" target="_blank">Gertrūda Valasevičiūtė</a> on <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText" target="_blank">Unsplash</a>)</span></td></tr></tbody></table><p style="text-align: center;"><span style="font-family: georgia;"></span></p><p style="text-align: justify;"><span style="font-family: georgia;"><b><i>ML is extracting data from knowledge.</i></b></span></p><p style="text-align: justify;"><span style="font-family: georgia;">Machine learning is a study of algorithms that uses a provides computers the ability to learn from the data and predict outcomes with accuracy, without being explicitly programmed. Machine learning is sub-branched into three categories- supervised learning, unsupervised learning, and reinforcement learning.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Machine Learning Model" src="https://cdn-images-1.medium.com/max/1000/0*pnD9ZzH91NeAszr3" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><p class="graf graf--p" name="400d"><span style="font-family: georgia; font-size: small;">(Image by Author) <strong class="markup--strong markup--p-strong">Machine Learning Model</strong></span></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Supervised learning</span></h2><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">As the name "supervised learning" suggests, here learning is based through example. We have a known set of inputs (called features, x) and outputs (called labels, y ). The goal of the algorithm is to train the model on the given data and predict the correct value (y) for an unknown input (x). Supervised learning can be further classified into two categories- classification and regression.</span></p><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;"><b>Classification</b> and <b>regression</b> are two basic concepts in supervised learning. However, understanding the difference between the two can be confusing and can lead to the implementation of the wrong algorithm for prediction. If we can understand the difference between the two and identify the algorithm that has to be used, then structuring the model becomes easy.</span></p><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">Classification and regression follow the <b>same basic concept</b> of supervised learning i.e. to train the model on a known dataset to make predict the outcome.</span></p><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">Here the major <b>difference</b> is that in the classification problem the output variable will be assigned to a category or class (i.e. it is discrete), while in regression the variable output is a continuous numerical value.</span></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Classification</span></h2><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">In classification, the model is trained in such a way that the output data is separated into different labels (or categories) according to the given input data. </span></p><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;"><b><i>The algorithm maps the input data (x) to discrete labels (y).</i></b></span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">Binary classification</span></h3><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">If there are only two categories in which the given data has to be classified then it is called binary classification. For example- checking a bank transaction whether it is a fraudulent or a genuine transaction. Here, there are only two categories (i.e. fraudulent or genuine) where the output can be labeled.</span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">Multiclass classification</span></h3><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">In this kind of problem, the input is categorized into one class out of three or more classes.</span></p><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;"><span class="markup--strong markup--p-strong"></span></span></p><p class="graf graf--p" name="400d" style="text-align: justify;"><span style="font-family: georgia;">Iris dataset is a perfect example of multiclass classification. Iris data set contains data of fifty samples of three species of flower (setosa, versicolor, and virginica) which are classified based on four parameters (sepal length, sepal width, petal length, and petal width).</span></p><p class="graf graf--p" name="400d" style="text-align: center;"><img alt="Iris Dataset Graphical Representation" src="https://cdn-images-1.medium.com/max/1000/0*jM3cSLzsSOpsZp-b" /></p><p class="graf graf--p" name="a7ea"><span style="font-family: georgia; font-size: small;">(Image by Author) <strong class="markup--strong markup--p-strong">Graphical representation of a linear discriminant model of Iris dataset</strong></span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">Two Kind of Classifiers</span></h3><p class="graf graf--p" name="a7ea" style="text-align: justify;"><span style="font-family: georgia;"><b>Soft Classifier</b></span></p><p class="graf graf--p" name="a7ea" style="text-align: justify;"><span style="font-family: georgia;">A soft classifier predicts the labels for inputs based on the probabilities. For a given input probability for each class (label) is calculated and the input is classified into the class with the highest probability. Higher probability also shows higher accuracy and precision of the model.</span></p><p class="graf graf--p" name="a7ea" style="text-align: justify;"><span style="font-family: georgia;"><span class="markup--strong markup--p-strong"></span></span></p><p class="graf graf--p" name="a7ea" style="text-align: justify;"><span style="font-family: georgia;">The sigmoid function can be used in this model since we have to predict the probabilities. This is because the sigmoid function exists between (0,1) and probability also exists between the same range.</span></p><p class="graf graf--p" name="a7ea" style="text-align: center;"><strong class="markup--strong markup--p-strong"><img alt="Sigmoid activation function formula" src="https://cdn-images-1.medium.com/max/1000/1*Mb4fSi5oKHxRjBcaB8Tr4Q.png" /></strong></p><p class="graf graf--p" name="a7ea" style="text-align: center;"><strong class="markup--strong markup--p-strong"></strong></p><p class="graf graf--p" name="505e"><span style="font-family: georgia; font-size: small;">(Image by Author) <strong class="markup--strong markup--p-strong">Sigmoid Function</strong></span></p></td></tr></tbody></table><p class="graf graf--p" name="15a3"><strong class="markup--strong markup--p-strong"><span style="font-family: georgia;">Hard Classifier</span></strong></p><p class="graf graf--p" name="a83f" style="text-align: justify;"><span style="font-family: georgia;">Hard classifiers do not calculate the probabilities for different categories and give the classification decision based on the decision boundary.</span></p><div style="text-align: justify;"><h3><span style="font-family: georgia; font-size: x-large;">Linear and Non- Linear Classifiers</span></h3><div style="text-align: center;"><img alt="Linear and Non- Linear Classifiers graphical representation" src="https://cdn-images-1.medium.com/max/1000/0*JMSl4YlM5I6fiKIR.png" /></div><div style="text-align: center;"><p class="graf graf--p" name="ac11"><span style="font-family: georgia; font-size: small;">(Image by <a class="markup--anchor markup--p-anchor" data-href="http://sebastianraschka.com/Articles/2014_naive_bayes_1.html" href="http://sebastianraschka.com/Articles/2014_naive_bayes_1.html" rel="noopener" target="_blank">Sebastian Raschka</a> on <a class="markup--anchor markup--p-anchor" data-href="https://commons.wikimedia.org/wiki/File:Main-qimg-48d5bd214e53d440fa32fc9e5300c894.png" href="https://commons.wikimedia.org/wiki/File:Main-qimg-48d5bd214e53d440fa32fc9e5300c894.png" rel="noopener" target="_blank">WikimediaCommons</a>) <strong class="markup--strong markup--p-strong">Graph A represents a linear classifier model. Graph B represents a non-linear classifier model.</strong></span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;"><b>Linear Classification Model</b></span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;">When the given data of two classes represented on a graph can be separated by drawing a straight line than the two classes are called linearly separable (in graph A above, green dots and blue dots, these two classes are completely separated by a single straight line).</span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;"><b><i>There can be infinite lines that can differentiate between two classes.</i></b></span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;">To find the exact position of the line, the type of classifier used is called a linear classifier. Few examples of linear classifiers are- Logistic Regression, Perceptron, Naive Bayes, etcetera.</span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;"><b>Non-Linear Classification Model</b></span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;">Here as we can see in graph B (above), two classes cannot be separated by drawing a straight line and therefore requires an alternative way to solve this kind of problem. Here model generates nonlinear boundaries and how that boundary will look like is defined by non-linear classifiers. Few examples of non-linear classifiers are- Decision Trees, K-Nearest Neighbour, Random Forest, etcetera.</span></p><h2 style="text-align: justify;"><span style="font-family: georgia;">Regression</span></h2><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;">Unlike classification, here the regression model is trained in such a way that it predicts continuous numerical value as an output based on input variables.</span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;"><b><i>The algorithm maps the input data (x) to continuous or numerical data(y).</i></b></span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;">There are several kinds of regression algorithms in machine learning like- linear regression, polynomial regression, quantile regression, lasso regression, etc. Linear regression is the simplest method of regression.</span></p><p class="graf graf--p" name="ac11" style="text-align: justify;"><span style="font-family: georgia;"><span class="markup--strong markup--p-strong"></span></span></p><h3 style="text-align: justify;"><span style="font-family: georgia;">Linear Regression</span></h3><div><img alt="Graphical Representation of Linear Regression Problem" src="https://cdn-images-1.medium.com/max/1000/0*U9LImEtU_SqVHK4I.png" /></div><div><p class="graf graf--p" name="5ba5"><span style="font-family: georgia; font-size: small;">(Image by <a class="markup--anchor markup--p-anchor" data-href="https://commons.wikimedia.org/w/index.php?title=User:Sewaqu&action=edit&redlink=1" href="https://commons.wikimedia.org/w/index.php?title=User:Sewaqu&action=edit&redlink=1" rel="noopener" target="_blank" title="User:Sewaqu (page does not exist)">Sewaqu</a> on <a class="markup--anchor markup--p-anchor" data-href="https://commons.wikimedia.org/wiki/File:Linear_regression.svg" href="https://commons.wikimedia.org/wiki/File:Linear_regression.svg" rel="noopener" target="_blank">WikimediaCommons</a>) <strong class="markup--strong markup--p-strong">Graphical Representation of Linear Regression Problem</strong></span></p></div><div style="text-align: justify;"><p class="graf graf--p" name="9925"><span style="font-family: georgia;">This approach is generally used for predictive analysis. In this case, a linear relationship is set up between the x-axis feature and the y-axis feature. But as you can see in the graph the line does not pass through every point, but it represents a relationship between the two.</span></p><p class="graf graf--p" name="084e"><span style="font-family: georgia;">Simple linear regression relation can be represented in the form of an equation as:</span></p><blockquote class="graf graf--pullquote" name="4f67"><span style="font-family: georgia;">y = wx + b</span></blockquote><p class="graf graf--p" name="a9f1"><span style="font-family: georgia;">Here, y is numerical output, w is the weight (slope), x is the input variable and b is the bias (or y-intercept).</span></p><p class="graf graf--p" name="b19c"><span style="font-family: georgia;">Regression models can be used in the prediction of temperature, trend forecast, <strong class="markup--strong markup--p-strong">analyze the effect of change of one variable on other variables.</strong></span></p><h2><span style="font-family: georgia; font-size: xx-large;">Conclusion</span></h2><p class="graf graf--p" name="7a53"><span style="font-family: georgia;">Supervised learning is the easiest and simplest sub-branch of machine learning. Identification of the correct algorithm to structure the model is very necessary and I hope you are able to understand the difference between regression and classification after reading this article. Try implementing these concepts for better understanding.</span></p><p class="graf graf--p" name="3cbd"><span style="font-family: georgia;">If you have any questions or comments, please post them in the comment section. </span></p><p class="graf graf--p" name="3cbd"><span style="font-family: georgia;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="506" src="https://www.youtube.com/embed/7vkkASg2xDk" width="900"></iframe></span></p><p class="graf graf--p" name="3cbd"><span style="font-family: georgia;"></span></p><pre class="graf graf--pre" name="6e23"><span style="font-family: georgia; font-size: small;">Sources:<br /><a class="markup--anchor markup--pre-anchor" data-href="https://developers.google.com/machine-learning/crash-course/ml-intro" href="https://developers.google.com/machine-learning/crash-course/ml-intro" rel="noopener" target="_blank">https://developers.google.com/machine-learning/crash-course/ml-intro</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://www.educative.io/edpresso/what-is-the-difference-between-regression-and-classification" href="https://www.educative.io/edpresso/what-is-the-difference-between-regression-and-classification" rel="noopener" target="_blank">https://www.educative.io/edpresso/what-is-the-difference-between-regression-and-classification</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://www.statisticssolutions.com/what-is-linear-regression/" href="https://www.statisticssolutions.com/what-is-linear-regression/" rel="noopener" target="_blank">https://www.statisticssolutions.com/what-is-linear-regression/</a><br /><a class="markup--anchor markup--pre-anchor" data-href="https://www.geeksforgeeks.org/ml-classification-vs-regression/" href="https://www.geeksforgeeks.org/ml-classification-vs-regression/" rel="nofollow noopener" target="_blank">https://www.geeksforgeeks.org/ml-classification-vs-regression/</a></span></pre></div></div></div>
Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com13tag:blogger.com,1999:blog-8707052728183762633.post-27659098613437924752020-07-30T06:25:00.004-07:002020-07-30T06:29:22.575-07:00Futuristic AI Connects Links in Past<span id="docs-internal-guid-43814215-7fff-7445-061e-ddc941952e79"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="The Martyrdom of Saint Serapion and Jan Asselijn’s The Threatened Swan MosAIc" height="293" src="https://lh6.googleusercontent.com/u8rwrp7k8EehZylHXUz-TLpSUvq4T16OPnx8aKt9xVCUjZTHooRrg6EP3J7q57sbOVJBHRt_xUA614PsYNLbL1ZEohOIjqH-_OioJThkISUtb_61VnEBfE7Bo9tUPSBqoZA8D14k=w640-h293" style="margin-left: auto; margin-right: auto; margin-top: 0px;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: Georgia; font-size: small;"><span style="white-space: pre-wrap;"><i>(Image Source: CSAIL MIT)<b> MosAIc</b></i></span></span></td></tr></tbody></table><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: center;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><i><b>“Art enables us to find ourselves and lose ourselves at the same time.” </b></i></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: right;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><i><b>-Thomas Merton</b></i></span></span></p><span style="font-family: georgia;"><br /></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">For some, art is a way of communication and for others, art is a deep mystery. A painting is just not just a surface with paint but it also represents the culture and history captured within it. Many times even for researchers it is not possible to figure out the connection between two completely different forms of art or paintings that might exist from different periods and space.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia;">What is MosAIc?</span></h2><div><br /></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">To find the most unexpected and obscure relation between parallel artworks at the Metropolitan Museum of Art (the Met) and Amsterdam’s Rijksmuseum MIT’s </span><a href="http://csail.mit.edu/" style="font-family: georgia; text-decoration-line: none; white-space: normal;" target="_blank"><span style="color: #0090eb; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Computer Science and Artificial Intelligence Laboratory</span></a><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"> (CSAIL) and Microsoft developers have created an artificial intelligence algorithm named as “MosAIc” system. This system will point out the similarities between art forms through different cultures and styles.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Researchers were inspired by the uncanny resemblance between </span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Francisco de Zurbarán’s “The Martyrdom of Saint Serapion”</span><span style="font-family: georgia; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> </span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">and Jan Asselijn’s “The Threatened Swan,” in the exhibit at “Rembrandt and Velazquez” in the Rijksmuseum</span><span style="font-family: georgia; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">. The interesting part is both the painters had never met each other in their entire life or had any correlation between them! </span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia;"><span style="white-space: pre-wrap;">A completely new algorithm has been developed to find the closest match by a particular artist or culture (according to the query of the user). An example stated at <a data-original-attrs="{"data-original-href":"https://www.csail.mit.edu/news/algorithm-finds-hidden-connections-between-paintings-met"}" href="#" target="_blank">csail.mit.edu</a>:</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">“In response to a query about “which musical instrument is closest to this painting of a blue-and-white dress,” the algorithm retrieves an image of a blue-and-white porcelain violin. These works are not only similar in pattern and form, but also draw their roots from a broader cultural exchange of porcelain between the Dutch and Chinese."</span></span></p><div><br /></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="MosAIc in working" src="https://lh4.googleusercontent.com/fB_k1SyJ9vVRyObmdM1QjnJF9DifBt4dBS3aBIvaymwnGZ_BSPY1NG5htr3f3ha4FX-BwIrG0-KGys69pcwFwTC_WoBCiekX5O440i4oV9Y81rWcOBTe0vm89xhEJL3Jnl0EDTD6=d" style="margin-left: auto; margin-right: auto; margin-top: 0px;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;"><i>(Image Source: <a data-original-attrs="{"data-original-href":"https://www.csail.mit.edu/news/algorithm-finds-hidden-connections-between-paintings-met"}" href="https://www.blogger.com/u/0/blog/post/edit/8707052728183762633/2765909861343792475#" target="_blank">CSAIL MIT</a>) <b>Results produced by MosAIc</b></i></span></span></p></td></tr></tbody></table><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></h2><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Technology Behind MosAIc</span></span></h2><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Today it is possible to generate a piece of art using AI (by GAN- generative adversarial network). However, MosAIc isn’t designed to create new art but to explore and find connections between existing art.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><br /></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This software can be compared to Google’s X Degree’s of Separation, as both try to create a path between different arts but the difference is MosAIc uses a single image to find a connection between different art (based on user query). </span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Developing the algorithm was a “tricky endeavor” says Mark Hamilton (lead author of the paper) because they should not only have a resemblance in color or style but theme and meaning as well.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">A new data structure was developed for searching the image i.e. “conditional KNN tree”. In simple words, similar images are grouped in this “tree”(it is called a tree because of its tree-like structure). The search starts from the “trunk” and continues to follow the branch where the similarity between the images is high and the closest image is found.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Outcome of MosAIc" src="https://lh3.googleusercontent.com/iMzfdyRWBcuSbUVRpV0lJfJYMdNojyUlu3MI6gKJua86s5lMx6DQ_qtF2cWIogTN6e-1rpYHvxKT1q0GK3phhydjLBs60T9o_tNpMPzHwaBBlZZL1fcY7VF_GP6-5YMydUWgQ0Fs=d" style="margin-left: auto; margin-right: auto; margin-top: 0px;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><i style="font-family: georgia; font-size: small; white-space: pre-wrap;">(Image Source: <a href="https://www.csail.mit.edu/news/algorithm-finds-hidden-connections-between-paintings-met" target="_blank">CSAIL MIT</a>) <b>Striking similarities between different art forms (results produced by MosAIc)</b></i></td></tr></tbody></table><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">This system will not only be limited to find a connection between different art but this same deep networks can be used to identify deepfakes (a form of fake media) that are generated by GANs. This algorithm will help to identify the “blind spots” (the areas in a dataset that are not accurately represented by GANs). It will be able to find the fake images that might look realistic to human eyes.</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Real and Deep Fake Images" src="https://lh4.googleusercontent.com/k3IsGWQyWKyNXrmyPjnmG8UTs_XFknSWfmZslVAssPfoWw7XEir6JNL1AOYv1toMLwwtusxaoi0CWjqlr_h9x_Sw1_T-yorsKwcE-OwJVcqUFuS5iK5dDTxCtyP_-9FzrpSq_YhM=d" style="margin-left: auto; margin-right: auto; margin-top: 0px;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><i style="font-family: georgia; font-size: small; white-space: pre-wrap;">(Image Source: <a href="https://www.csail.mit.edu/news/algorithm-finds-hidden-connections-between-paintings-met" target="_blank">CSAIL MIT</a>) <b>Identification of deep fakes</b></i></td></tr></tbody></table><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></h2><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">Our Take on This:</span></span></h2><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><br /></span></span></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;">The lethal combination of art and artificial intelligence is opening new avenues and opportunities for future research and as well as for understanding our diverse history and culture. </span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: center;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><b><i>“A picture is a poem without words.”</i></b></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: right;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><b><i> -Horace</i></b></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: right;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia;"><b><br /></b></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;">Bibliography:</span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://www.csail.mit.edu/news/algorithm-finds-hidden-connections-between-paintings-met" style="text-decoration-line: none;" target="_blank"><span style="color: #1155cc; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;">CSAIL MIT</span></span></a></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://www.techrepublic.com/article/mit-and-microsoft-algorithm-determines-correlations-in-vast-art-collections/" style="text-decoration-line: none;" target="_blank"><span style="color: #1155cc; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;">Techrepublic</span></span></a></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="color: #1155cc; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><span style="font-family: georgia; font-size: small;"><a data-original-attrs="{"data-original-href":"https://www.zdnet.com/article/this-ai-can-discover-the-hidden-links-between-great-works-of-art/"}" href="#" style="text-decoration-line: none;" target="_blank">Zdnet</a></span></span></p></span>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com0tag:blogger.com,1999:blog-8707052728183762633.post-89928590118020986312020-07-29T09:45:00.002-07:002020-07-29T10:18:02.963-07:00Drones are Helping Protect Bird Nests<span id="docs-internal-guid-908cb880-7fff-975e-fb49-9f5c5824053d"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The advancement of mankind has for long been a threat to wildlife. The latest threats have come to the nesting birds on agricultural grounds which are being displaced from their habitats owing to agricultural activities.</font></span></p><div style="text-align: justify;"><span style="white-space: pre-wrap;"><font face="georgia">The integration of drones along with thermal imaging cameras powered by artificial intelligence can be promising for the conservation of birds.</font></span></div><div style="text-align: justify;"><span style="white-space: pre-wrap;"><font face="georgia" size="5"><b>What is the current technology?</b></font></span></div><div><span id="docs-internal-guid-134351c6-7fff-5f65-dcd3-61d67f51a9d8"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Physical inspections and thermal imaging on the ground have been practiced for years but not without its fair share of problems such as; dense vegetation, objects along the path of inspection.</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: georgia; white-space: pre-wrap;"><b><font size="5">What has been proposed?</font></b></span></p></span></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Thermal Imaging of Farm" height="384" src="https://lh5.googleusercontent.com/xBNJsuCY3ED-XZlP6cy_o6tRE8wt_z_85aSRElT9sgrBMhNywcNcASWWG-6KSCF0J-rJCZ01zlEV9d4G-mRmy7HaN2tweDpYNqGd-3-_3OoaLrlRPWB2p__X_sAUJ-HaGZFx2gtm=w640-h384" style="margin-left: auto; margin-right: auto; margin-top: 0px;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><a href="https://www.nature.com/articles/s41598-020-67898-3/figures/3" target="_blank"><font size="2">Integration of System</font></a></td></tr></tbody></table><div><span id="docs-internal-guid-bb5ad2ec-7fff-caf2-68ae-c2c8e0f61ddb"><span style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></span></div><div><span id="docs-internal-guid-c3719b7e-7fff-7654-7887-422d421b556c"><font face="georgia"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Researchers have devised an arrangement of drones equipped with thermal imaging sensors that will capture multiple heat-sensitive images which will be fed into an Artificial Intelligence-powered software to accurately map nests and inform co-ordinates for the nest which can be relocated before agricultural activities take place.</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></p><blockquote><font size="5">“The system works best under cloudy and cold conditions surveying uneven grounds.”</font></blockquote></font></span><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The system is currently being tested in Southern Finland with the test subject being a Northern lapwing bird.</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5"><br /></font></span></p><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span id="docs-internal-guid-42188c34-7fff-d58a-1e3e-5aaba03da82f"><h3 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><font face="georgia" size="5"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">Ho</span><span>w was the study conducted?</span></font></h3><div style="font-size: 11pt;"><font face="georgia"><span style="font-size: 14.6667px;"><br /></span></font></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="font-size: 11pt; margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Thermal Imaging" height="440" src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-020-67898-3/MediaObjects/41598_2020_67898_Fig4_HTML.png" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><a href="https://www.nature.com/articles/s41598-020-67898-3/figures/4" target="_blank"><font size="2">Steps Under the Study</font></a></td></tr></tbody></table><div style="font-size: 11pt;"><font face="georgia"></font></div><div><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><ul style="margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">An appropriate field was chosen for the study (<i>fig.A)</i></font></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The drone is flown over a pre-programmed route<i>(fig.B)</i></font></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Thermal images are prepared by extracting co-ordinates of the boxes drawn around the nest.<i>(fig.C)</i></font></span></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><font face="georgia"><span style="background-color: white; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> A neural network deep learning algorithm is applied to classify whether there is the presence of a nest<i>. </i></span><span style="background-color: white; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><i>(fig.D)</i></span></font></p></li></ul><div><font color="#222222" face="georgia"><br /></font></div><span id="docs-internal-guid-c7b8e39a-7fff-24fe-49c4-5620d4cb5014"><h3 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: left;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><font face="georgia" size="5">Our Take on This</font></span></h3><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><font face="georgia">The use of drones in agriculture is ever-growing finding it’s application in precision agriculture, to monitor the real-time spread of disease amongst crops and maximizing efficiency. It takes off physical labor from the shoulders of humans while increasing the accuracy of detection. We believe integrating it into traditional agriculture would be a groundbreaking move when the world strives to move an inch closer to saving nature.</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><font face="georgia">It would be worth watching how it behaves under different climatic conditions, habitat, and species.</font></span></p><p dir="ltr" style="font-size: 11pt; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;"><font face="georgia" size="2">Bibliography:</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><font face="georgia" size="2"><a href="https://www.nature.com/articles/s41598-020-67898-3#Sec2" target="_blank">Nature</a></font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><font face="georgia" size="2"><a href="https://www.futurefarming.com/Tools-data/Articles/2020/7/Protecting-farmland-bird-nests-with-drones-and-AI-616019E/" target="_blank">Future Farming</a></font></p></span></span></div></span></span></div></div></span>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com0tag:blogger.com,1999:blog-8707052728183762633.post-57614810050325632592020-07-26T02:42:00.002-07:002020-07-26T02:42:55.203-07:00AI Plays Tetris and Beats World Record<span id="docs-internal-guid-7ba8a444-7fff-0927-e920-7977b0d46ce9"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Cube, Tetris, Play, Blocks, artificial intelligence" src="https://cdn.pixabay.com/photo/2016/09/18/20/39/cube-1678974_960_720.png" style="margin-left: auto; margin-right: auto; text-align: left;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><font size="2">Tetris Game</font><br /></td></tr></tbody></table><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The ever-growing power of artificial intelligence never stops to amaze us. Now, it sets a mind-blowing world record.</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Australian coder </span><a href="https://www.youtube.com/watch?v=QOJfyp0KMmM" style="text-decoration-line: none;" target="_blank"><span style="color: black; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Code Bullet</span></a><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> created an artificial intelligence program that beat the current world record for the longest Tetris game of 4,988 lines which is currently held by gamer Harry Hong. Even the whole Tetris game was programmed by the coder. It seems impressive to see how he builds everything from scratch and breaks the world record!</span></font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></font></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">What is A.I. Tetris?</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Tetris is a tile-matching video game where the goal is to destroy the lines of tiles (or blocks) strategically to prevent them to stack to the top of the screen. Human intelligence supported with artificial intelligence showed some marvelous results for Tetris. The video uploaded by Bullet shows A.I. has cleared about 14,000 lines of the blocks in the game of Tetris. He also mentions in the video that the A.I.-powered game can go up to infinite lines. </font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: center;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><i><b>"Playing 'Tetris' for 15 minutes is like meditation." </b></i></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: right;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><i><b>~ Ezra Koenig</b></i></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: right;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><i><b><br /></b></i></font></span></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">How Does A.I. Tetris Works?</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">This game works on a simple algorithm that is to pick the best move out of all the possible moves, which is the “backbone” of artificial intelligence. Good moves and bad moves are classified by assigning points depending on some parameters and the one with the highest score is considered the best move (reinforced learning). </font></span></p><ul style="margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><u><b>Minimize global holes:</b></u></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> block spaces that are left empty between or under your pieces and cannot be filled further (these holes are considered bad), will result in fewer points to that move i.e. it will be considered a bad move. So A.I. will optimize itself by selecting the moves which will have least or no spaces left.</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><u><b>Minimize the height of stacks:</b></u></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> only reducing the holes is not enough, stack height should also be minimum as the purpose is to eliminate complete rows and prevent stack to reach the top of the screen. So, this parameter plays an important role in the algorithm.</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><b><u>Check hold piece:</u></b></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> (it is a piece at the top corner which can be exchanged by the current piece until it locks) best end position of the current piece is compared with that of the hold piece and the better one is used.</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><u><b>Minimize empty pillars:</b></u></span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> further optimization of the algorithm requires filling of empty pillars (since they can only be filled by line pieces, and needs to be minimized), this problem is solved by penalizing the moves which resulted in an empty pillar of three or more blocks. </span></font></p></li></ul><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">And this is how artificial intelligence-powered Tetris broke the world record (however, there is no legitimacy of the record as the whole game was programmed by the programmer himself and was not official Tetris).</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Our Take on This</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">We feel that although it is really cool to have AI perform such tasks, it is useless and not really a directed approach to improving the general intelligence of AI by making it well-versed in gaming. A human playing Tetris might have used a multitude of skills to reach a certain level, although AI does not develop skills because of training under an infinite-sample of data for a specific task.</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="506" src="https://www.youtube.com/embed/QOJfyp0KMmM" width="900"></iframe></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font size="2">Bibliography:</font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><a href="https://nerdist.com/article/coder-tetris-ai-world-record/" target="_blank"><font size="2">https://nerdist.com/article/coder-tetris-ai-world-record/</font></a></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><a href="https://geekologie.com/2020/07/guy-codes-own-tetris-program-and-then-cr.php" target="_blank"><font size="2">https://geekologie.com/2020/07/guy-codes-own-tetris-program-and-then-cr.php</font></a></p></span>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com2tag:blogger.com,1999:blog-8707052728183762633.post-57112832160904663422020-07-24T10:27:00.003-07:002020-07-26T02:03:17.427-07:00Is Laser-Textured Metal the Next Big Thing?<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><font face="georgia"><img alt="copper nugget" height="400" src="https://upload.wikimedia.org/wikipedia/commons/5/59/Natural_copper_nugget.jpg" style="margin-left: auto; margin-right: auto;" width="400" /></font></td></tr><tr><td class="tr-caption" style="text-align: center;"><font face="georgia"><a href="https://commons.wikimedia.org/wiki/File:Natural_copper_nugget.jpg" target="_blank"><font size="2">Copper Nugget</font></a><br /></font></td></tr></tbody></table><span style="text-align: justify;"><font face="georgia">In
a world fueled by technology and science, it’s only about time groundbreaking
discoveries are made. This is it.</font></span><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span><font face="georgia">Science has always been a keen believer
in borrowing from the past to propel the future. It’s always been of interest
to us in creating ways to make the world a safer and better place. </font></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">The
findings on how to fight bacteria are revolutionary and could limit the spread
of many such pathogens who live on surfaces for days to find their next prey.</font></p><h2 style="text-align: justify;"><b><font face="georgia" size="5">What has been happening until now?</font></b></h2>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">Ever
since the early age’s humans have been finding ways to discover surfaces that
inhibit the growth of viruses and Copper is one such material which has passed
the course of time.</font></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">Copper’s
antimicrobial properties have made it a prominent candidate for storing food
and water, especially in India. </font></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">The
surface of copper is conductive to electricity and in the case of a microbe
touching its surface, electrons are carried away from the microbe’s membrane,
ultimately resulting in the destruction of the organism.</font></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;">
<font face="georgia"><!--[endif]--><o:p></o:p></font></p>
<h2 style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><b><font face="georgia" size="5">What is the proposed way? </font></b></h2><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;">
<font face="georgia"><!--[endif]--><o:p></o:p></font></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">To
enhance the antimicrobial properties of copper was hit with laser for a
couple of milliseconds which resulted in nano-scale pores on the surface of the
metal ultimately increasing the surface area. <span style="font-size: 12pt;"><o:p></o:p></span></font></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">This
increases the probability of the bacteria hitting its surface and rupturing.
The rugged surface of the metal clings better to water and thus increases the
chance of killing any bacteria present in it.</font></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="559" src="https://www.youtube.com/embed/3vFFdNXsoN0" width="994"></iframe></font></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia"><o:p></o:p></font></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia" size="2"> Treating metal surface</font></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><b><font face="georgia" size="5">What have been the observations?</font></b></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia"><o:p></o:p></font></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia">Several bacterial strains such as the<b> </b><b><i><span style="background: white;">Escherichia
coli</span></i></b><span style="background: white;"> and a drug-resistant <b><i>Staphylococcus</i></b> <b><i>aureus</i></b>
strain were tested on both treated and untreated surfaces and the findings were
listed as :</span><o:p></o:p></font></p>
<ul style="margin-top: 0cm;" type="disc">
<li class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: justify; vertical-align: baseline;"><font face="georgia"><span style="background: white;">The strains on hitting the laser-hit surface were
immediately showing signs of membrane damage; eradicating the bacteria
much quicker than the untreated surface.</span><o:p></o:p></font></li>
<li class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: justify; vertical-align: baseline;"><span style="background: white;"><font face="georgia">Some microbes were immediately killed while colonies of
bacteria took anywhere from 40 minutes to 2 hours depending upon strain
and their concentration.</font></span></li></ul><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;"><font face="georgia"><o:p></o:p></font></span></p>
<h2 style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><font face="georgia" size="5"><b><span style="background: white;">What is next for the discovery?</span></b></font></h2>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="background-color: white;"><font face="georgia">The application of this technique can have
far-fetching effects such as:</font></span></p>
<ul style="margin-top: 0cm;" type="disc">
<li class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: justify; vertical-align: baseline;"><font face="georgia"><span style="background: white;">Reduction of </span><span style="background: white;">bacterial growth and biofilm formation on
orthopedic implants which in turn reduces the need to give antibiotics to
kill bacteria from the implant surface.</span><o:p></o:p></font></li>
<li class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: justify; vertical-align: baseline;"><font face="georgia"><span style="background: white;">The rugged texture results in a hydrophilic surface,
which in the case of the orthopedic implant results in improved
integration with the bone.</span><o:p></o:p></font></li>
<li class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: justify; vertical-align: baseline;"><font face="georgia"><span style="background: white;">There is no application of coating making them
environment friendly.</span></font></li></ul><h2 style="text-align: justify;"><font face="georgia" size="5">Our Take on This</font></h2><div style="text-align: justify;"><font face="georgia">I feel that this can be a game-changer because of the ease of integration onto the current products without driving up costs.</font></div>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: small;"><font face="georgia">Bibliography:</font></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span><o:p><font face="georgia" size="2"><a href="https://www.scientificamerican.com/article/laser-textured-metal-surfaces-kill-bacteria-faster/#:~:text=Copper%20surfaces%20kill%20microbes%20that,by%20zapping%20it%20with%20lasers." target="_blank">Scientific American</a></font></o:p></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span><o:p><font face="georgia" size="2"><a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/admi.201901890" target="_blank">Wiley Online Library</a></font></o:p></span></p>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com0tag:blogger.com,1999:blog-8707052728183762633.post-78191937534068761782020-07-22T23:10:00.003-07:002020-08-25T00:26:47.542-07:00Escape Reality With the Power of AI<span id="docs-internal-guid-bc81981c-7fff-1d3d-acef-54b4e7f2690a"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Roomality Virtual Window VR" src="https://lh3.googleusercontent.com/pN5x-PD4yCJZR5hg9C4KgcHrB-AT56phnzJitDpkLrFbXkg3Xr5Yuv2-TRvNy-KEUUax65cPaOpNXvdP637BgcI66kmnbgXlEU4hsNfC03kP4zswv4Avic8pI-AjA6kUyJdY_vgZ=d" style="margin-left: auto; margin-right: auto; margin-top: 0px;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><font size="2"><a href="https://roomality.com/" target="_blank">(Image source)</a> Roomality Virtual Window</font><a href="https://roomality.com/" target="_blank"><br /></a></td></tr></tbody></table><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-align: justify; vertical-align: baseline; white-space: pre-wrap;">Transform your surrounding into an entirely new world, immerse yourself into a virtually real-life landscape simply with the power of AI. </span><a href="https://roomality.com/" style="text-align: justify;" target="_blank"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Roomality</span></a><span style="font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; text-align: justify; vertical-align: baseline; white-space: pre-wrap;"> </span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-align: justify; vertical-align: baseline; white-space: pre-wrap;">is a realistic, 3D system that lets users experience a real-world environment at their home or office window. Roomality is a breakthrough in the field of virtual reality, it gives a mesmerizing feel of “virtually real world” to the user, a</span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; text-align: justify; vertical-align: baseline; white-space: pre-wrap;">ll </span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; text-align: justify; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">without the need of a headset, goggles or glasses!</span></font><h2 dir="ltr" style="line-height: 1.38; margin-bottom: 6pt; margin-top: 18pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5">What is Roomality?</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Roomality (Ireland based start-up) is an AI-based system, that simply can be can be called as “virtual windows”. This tool projects an immersive 3D scene onto a user’s surrounding with </span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">objects and distances being rendered in real-time.</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> </span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Users can customize there surrounding according to there mood and need, can explore the world and this system can even teleport you to the surface of the moon!</span></font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="642" src="https://www.youtube.com/embed/PJcdyE606rI" width="1139"></iframe></p><h2 dir="ltr" style="line-height: 1.38; margin-bottom: 6pt; margin-top: 18pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5">What is Technology Behind Roomality?</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The system tracks the user’s eye movements with the help of the cameras positioned over the window and is powered by AI. With the movement of eyes, the display is constantly adjusted to provide a fully interactive and splendid experience to the user. For artificially recreating real-world environments and to provide photo-realistic, high-resolution 3D details in the display, the company is using Unreal engine and Quixel Megascans. This is how this technology lets users experience the artificial world without any wearable headgear.</font></span></p><font face="georgia"><br /></font><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The company plans to develop this project in 3 phases:</font></span></p><ul style="margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Phase 1 (completed):</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> At this stage, the company ensures that the system is successfully able to track the eye movement and projection of the scene on the large display. “</span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">This allows the user to feel as though they can walk right out and enter this new world”.</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Phase 2 (on-going):</span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> At this stage, Romality will be projected onto an entire wall with the added dimension of french doors, a large balcony, or a full walled window, letting the users feel a virtual perception of stepping out and exploring the terrace.</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Phase 3 (planned):</span><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> In the final phase, Roomality will not only be limited to a screen or wall but all over the room, giving a dome-like effect. This stage will give users a “totally immersive environment” as they will be able to navigate the virtual environment just by looking anywhere in the room.</span></font></p></li></ul><h2 dir="ltr" style="line-height: 1.38; margin-bottom: 6pt; margin-top: 18pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5">How Roomality can be Used?</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">This system can be used for leisure purposes and as well as for professional purposes:</font></span></p><ul style="margin-bottom: 0px; margin-top: 0px;"><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Personal use:</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> This can be a great tool to escape reality for a while. Users can select from a variety of landscapes to relax, enjoy, self-development, explore, and etcetera. </span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Exploring the World:</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> Travel the whole world just by sitting in your room. Transport yourselves to a beautiful beach, relax in a peaceful forest and explore the unexplored!</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Gaming:</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> Gaming experience can be completely evolved with this system. Physical interaction will eliminate the need for any wearable VR headset and elevate the overall experience by completely immersing the gamer “inside” the game.</span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Architectural Design:</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> With the ever-growing need for accommodation, hotel rooms, etc. rooms can be easily decorated (especially windowless accommodations) with this technology without any need for physical space or construction. This solution can ease the work of the designers and architects to a great extent. </span></font></p></li><li dir="ltr" style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><font face="georgia"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">The Film Industry:</span><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> Firstly, this system will help to greatly reduce the cost of production by providing an alternative to the actual location. Moreover, Roomality can replace the need for the use of a green screen (which produces a low quality, unrealistic films) by providing a high-quality 3D realistic display. </span></font></p></li></ul><font face="georgia"><br /></font><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">This system is not only limited to these but it has the potential of endless possibilities that can be explored by the user. </font></span></p><font face="georgia"><br /></font><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: center;"><span style="background-color: white; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><b>“VR is a way to escape the real world into something more fantastic. It has the potential to be the most social technology of all time.”</b></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: right;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">-Palmer Luckey, Founder of Oculus Rift</font></span></p><h2 dir="ltr" style="line-height: 1.38; margin-bottom: 6pt; margin-top: 18pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5">Pros and Cons of Roomality</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Roomality lets you escape your daily mundane routine and takes to you to a new world. This can be a good tool for recreation and self-growth. Roomality will also simplify many complex situations in several professions (as some of them mentioned above). </font></span></p><font face="georgia"><br /></font><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The only drawback of Roomality is that at the current stage this system will only work accurately when only one person enters the room. This is because of the AI technology and cameras mounted which can track the eyes of one person at a time. </font></span></p><h2 style="line-height: 1.38; margin-bottom: 6pt; margin-top: 18pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5">Our Take on Roomality</font></span></h2><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Roomality is no doubt a breakthrough in the virtual world, it may serve several purposes, but we firmly believe that the artificial world will never replace the real world! </font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="642" src="https://www.youtube.com/embed/CD59L9veayk" width="1139"></iframe></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="2">Bibliography:</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><a href="https://roomality.com/" target="_blank"><font size="2">https://roomality.com/</font></a></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://venturebeat.com/2020/07/21/roomality-uses-ai-to-render-window-sized-3d-landscapes-without-glasses/" target="_blank"><font size="2">https://venturebeat.com/2020/07/21/roomality-uses-ai-to-render-window-sized-3d-landscapes-without-glasses/</font></a></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://www.irishtimes.com/business/technology/irish-entrepreneur-promises-vr-experience-without-goggles-or-headsets-1.4310225" target="_blank"><font size="2">https://www.irishtimes.com/business/technology/irish-entrepreneur-promises-vr-experience-without-goggles-or-headsets-1.4310225</font></a></p></span>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com1tag:blogger.com,1999:blog-8707052728183762633.post-28099958152458278662020-07-20T13:30:00.001-07:002020-07-20T13:30:03.837-07:00Amazon Drones Will Never be Lost Again!<span id="docs-internal-guid-aecb37da-7fff-8d2c-3ae0-e30d7dbbf2c3"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Amazon navigation beacon drone" src="https://lh3.googleusercontent.com/1MGz-vZgvBT_4kmMVLzgLrThpS3Lla4suytff-QSPubkj143_2IHu7wO97rjukgL24OVVg-IX6jyxBRCNPJV7WUSR6wVkTBSTuKvjf5wRsjRQp5IDSQOCYVPJH0VcddZ2NK7zS2p=d" style="margin-left: auto; margin-right: auto; margin-top: 0px;" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><a href="https://pdfpiw.uspto.gov/.piw?PageNum=0&docid=10710719&IDKey=D69D1E6109AF&HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2Fnph-Parser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526p%3D1%2526u%3D%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r%3D34%2526f%3DG%2526l%3D50%2526co1%3DAND%2526d%3DPTXT%2526s1%3Damazon.ASNM.%2526OS%3DAN%2Famazon%2526RS%3DAN%2Famazon" style="font-family: georgia; font-size: small; white-space: pre-wrap;" target="_blank">Amazon's Concept Drone</a></td></tr></tbody></table><span style="font-family: georgia; text-align: justify; white-space: pre-wrap;"><br /></span></span><div><div style="text-align: left;"><span style="text-align: justify;"><font face="georgia">Amazon’s latest patent shows that the company will be soon coming up with its latest innovation “an unconventional idea” for drones that will revolutionize the modern world of technology. Amazon's idea for unmanned aerial vehicles (UAV's) is similar to the concept of maple seeds! Basically, this concept drone (or UAV) is designed to drop small devices (sensors) which will work as a navigation beacon for the UAV in any case of the system fails to track position and orientation of the drone.</font></span></div><div style="text-align: left;"><span style="font-family: georgia; text-align: justify; white-space: pre-wrap;">Nowadays drones have been utilized for numerous kinds of purposes like remote sensing, </span><span style="font-family: georgia; text-align: justify; white-space: pre-wrap;">aerial surveillance, recreational purposes, crisis response, commercial purposes (like quicker shipping and delivery of products), and several others. And at the same time, while operating in a low visibility area, operating too close to the ground, obstruction of the camera and sensor (GPS, stereo camera failure, etc.), or for any other technical reason the equipment may fail to achieve those successful outcomes that are desired.</span></div><div style="text-align: left;"><span style="font-family: georgia; text-align: justify; white-space: pre-wrap;">For the successful execution of the task, concept UAV has been equipped with a backup system that will save the sensor data in case of sensor malfunction. The drone will disperse navigation beacons which will be detected by the device and help it land proximate to beacons. </span></div><span><h2 style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">How will these drones work?</font></span></h2><div class="separator" style="clear: both; text-align: center;"><span style="border: none; display: inline-block; height: 377px; margin-left: 1em; margin-right: 1em; overflow: hidden; width: 297px;"><img alt="Amazon navigation beacon drone" src="https://lh5.googleusercontent.com/CVDtbaiGm6rsbDarg25HNuBEyjL0ylzOh-ZBwLtYTphdP9JWXO1kt2gZ0WySv7hEwrGV7nB232mxOvBkaSHi7eloJpOKDY5sYsLqkV2qUf_qCCBOsHuDUZSAlXvk3TnzvjiaSu4g=d" style="margin-left: 0px; margin-top: 0px;" /></span></div><div><span id="docs-internal-guid-a900f377-7fff-937c-4837-6a807fa3bc79"><span style="font-family: Raleway, sans-serif; font-size: 14pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></span></div><font face="georgia" size="2"><div style="text-align: center;"><span id="docs-internal-guid-ed5c5e50-7fff-959d-0d70-190cf2099574"><span style="color: #1155cc; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"><a href="https://pdfpiw.uspto.gov/.piw?PageNum=0&docid=10710719&IDKey=D69D1E6109AF&HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2Fnph-Parser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526p%3D1%2526u%3D%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r%3D34%2526f%3DG%2526l%3D50%2526co1%3DAND%2526d%3DPTXT%2526s1%3Damazon.ASNM.%2526OS%3DAN%2Famazon%2526RS%3DAN%2Famazon" target="_blank">Working of Navigation Beacon Drone</a></span></span></div></font><ul style="margin-bottom: 0px; margin-top: 0px; text-align: left;"><li><span id="docs-internal-guid-aecb37da-7fff-8d2c-3ae0-e30d7dbbf2c3"><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="font-family: georgia; white-space: pre-wrap;">If a fault preventing the device from precisely and accurately recognize its position, is detected in the drone’s system, the first set of navigation beacons are deployed from the drone.</span></p></span></li><li><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="font-family: georgia; white-space: pre-wrap;">Now beacons determine the attributes of the environment.</span></p></li><li><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-family: georgia; white-space: pre-wrap;">If there is any failure of navigation beacons or UAV is not able to resolve the location of beacons or to detect the lateral features of the environment, the second set of navigation beacons is deployed and then location is tracked.</span></p></li><li><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-family: georgia; white-space: pre-wrap;">The navigation beacons will output a signal (depending on the environment detected) that can be a visual, sound, or a radio signal. But the signal of each beacon might not be the same, they can have different wavelengths, as for the drone to identify the location of each beacon.</span></p></li><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Then UAV may lead to land proximate to the detected surface and prevent the loss of sensor data.</font></span></p></li></ul><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">A detailed description of techniques, apparatuses and components of concept drones is available in <a href="https://pdfpiw.uspto.gov/.piw?PageNum=0&docid=10710719&IDKey=D69D1E6109AF&HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2Fnph-Parser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526p%3D1%2526u%3D%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r%3D34%2526f%3DG%2526l%3D50%2526co1%3DAND%2526d%3DPTXT%2526s1%3Damazon.ASNM.%2526OS%3DAN%2Famazon%2526RS%3DAN%2Famazon" target="_blank">Amazon's patent</a>. </font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The concept of navigation beacon drone seems magnificent, but it is still unclear when Amazon will produce it.</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Check out Amazon's another impressive innovation<a href="https://patataeater.blogspot.com/2020/07/amazon-smart-shopping-cart-dash-cart.html" target="_blank"> here.</a></font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="2">Bibliography:</font></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><a href="https://pdfpiw.uspto.gov/.piw?PageNum=0&docid=10710719&IDKey=D69D1E6109AF&HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2Fnph-Parser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526p%3D1%2526u%3D%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r%3D34%2526f%3DG%2526l%3D50%2526co1%3DAND%2526d%3DPTXT%2526s1%3Damazon.ASNM.%2526OS%3DAN%2Famazon%2526RS%3DAN%2Famazon" style="text-align: left;" target="_blank"><font size="2">https://pdfpiw.uspto.gov/.piw?PageNum=0&docid=10710719&IDKey=D69D1E6109AF&HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2Fnph-Parser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526p%3D1%2526u%3D%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r%3D34%2526f%3DG%2526l%3D50%2526co1%3DAND%2526d%3DPTXT%2526s1%3Damazon.ASNM.%2526OS%3DAN%2Famazon%2526RS%3DAN%2Famazon</font></a></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><font size="2"><a href="https://patents.justia.com/patent/10710719" rel="nofollow" target="_blank">https://patents.justia.com/patent/10710719</a></font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><font size="2"><a href="https://www.protocol.com/amazon-patent-location-tracking-beacons" target="_blank">https://www.protocol.com/amazon-patent-location-tracking-beacons</a></font></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 12pt; margin-top: 12pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="2"><br /></font></span></p><div style="text-align: justify;"><br /></div></span></div>Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com1tag:blogger.com,1999:blog-8707052728183762633.post-21814253609794048402020-07-20T06:06:00.001-07:002020-07-20T06:06:13.251-07:00Proteus : The World's First Non-Cuttable Material<span id="docs-internal-guid-d0285532-7fff-77a5-4a54-811654425fef"><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia; white-space: pre-wrap;">The material inspired by shells and grapefruits can turn back the force of a cutting tool upon itself.</span></p><font face="georgia"><div style="text-align: justify;"><span style="white-space: pre-wrap;"><br /></span></div><div style="text-align: justify;"><span style="white-space: pre-wrap;">Named Proteus after the shape-changing god, the material is constructed of ceramic spheres encased in cellular aluminium structures that cannot be cut even by water jets.</span></div></font><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The tough cellular skins of the grapefruit and the fracture-resistant shell of molluscs are the core of ideation of the new material. </font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div></font><h2 style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="5"><b>How exactly is the idea inspired by nature? </b></font></span></h2><font face="georgia"><div style="text-align: justify;"><br /></div></font><ul style="margin-bottom: 0px; margin-top: 0px; text-align: left;"><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The free fall of a grapefruit does not damage it’s pulp because it’s peel made up of vascular bundles and an open pore cellular structure.</font></span></p></li><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Arapaimas fish resist the attack of a piranha’s teeth through the hierarchical design of their scales.</font></span></p></li><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Narce of Abalon ( in simpler terms, mother of pearl) has hard organic, an inflexible layer </font></span></p></li></ul><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The system is dynamic with an evolving internal structure that creates high-speed motion when it interacts with cutting tools.</font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The material is made from a cellular aluminium structure wrapped around ceramic spheres which is destructive for cutting tools.</font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Proteus Material | Maximum Impact of grinder" height="267" src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-020-65976-0/MediaObjects/41598_2020_65976_Fig24_HTML.jpg" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><a href="https://www.nature.com/articles/s41598-020-65976-0/figures/24" target="_blank">Maximum Impact of Angle Grinder on sample</a><br /></td></tr></tbody></table><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><br /></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">When a cutting tool encounters the ceramic sphere, localized load on the rim of the rotating disc is produced which ultimately leads to high-frequency, out of plane vibrations.</font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Instead of preventing this fragmentation by using a harder material than aluminium, embracing it because the fine particulate matter produced is useful for the abrasion of disc used to cut the material.</font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">In simpler words, the interaction between the cutting disc and the sphere creates an interlocking and the blade becomes blunt since it’s energy and force is turned upon itself.</font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Water jets are also ineffective since the curved sphere widens the spread of the jet, reducing the speed.</font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The analogy stated by Lead Author <b>Dr Stefan Szyniszweski</b> is given as:</font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div></font><p style="line-height: 1.38; margin-bottom: 0pt; margin-left: 36pt; margin-top: 0pt; text-align: justify;"><span style="background-color: white; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><b><i>“Essentially cutting our material is like cutting through a jelly-filled with nuggets. If you get through the jelly you hit the nuggets and the material will vibrate in such a way that it destroys the cutting disc or drill bit.”</i></b></font></span></p><font face="georgia"><div style="text-align: justify;"><br /></div><h2 style="text-align: justify;"><b style="white-space: pre-wrap;"><font size="5">How is Proteus manufactured?</font></b></h2></font><font face="georgia"><div style="text-align: justify;"><br /></div></font><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="Mnufacturing of Proteus" height="229" src="https://lh5.googleusercontent.com/mH8Phq-k_k73iRyEY_ehTwj5GkzlJc63DHiVp8TT_IcI_167293r7h6QXjKD_xTwCXN1P9DTQ4eLMlWHaBWkU49IbWAzcdipg60p2J_BicJwCL9_fjzF64h-WHIz_BkT17_neiPI=w640-h229" style="margin-left: auto; margin-right: auto; margin-top: 0px;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><a href="https://www.nature.com/articles/s41598-020-65976-0/figures/2" target="_blank">Manufacturing of Proteus</a></td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><font face="georgia" style="margin-left: 1em; margin-right: 1em;"></font></div><ul style="margin-bottom: 0px; margin-top: 0px; text-align: left;"><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="background-color: white; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: transparent; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">Manufacturing steps include mixing metal powder with a small amount of foaming agent. </font></span></p></li><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="background-color: white; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="background-color: transparent; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">It is followed by pressing the mix and extrusion into preform shapes. </font></span></p></li><li style="font-variant-east-asian: normal; font-variant-numeric: normal; list-style-type: disc; vertical-align: baseline; white-space: pre;"><p role="presentation" style="background-color: white; line-height: 1.38; margin-bottom: 18pt; margin-top: 0pt; text-align: justify;"><span style="background-color: transparent; color: #222222; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The compressed powder bars enable precise placement of ceramic segments in the orthogonal pattern and manufacturing of the end product in an industrial furnace.</font></span></p></li></ul><div style="text-align: justify;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="520" src="https://www.youtube.com/embed/S8no-4z9hbM" width="918"></iframe></div><div style="text-align: justify;"><font color="#222222" face="georgia"><span style="white-space: pre-wrap;"> <b><font size="2">Angle Grinder Attack on Proteus</font></b></span></font></div><div style="text-align: justify;"><font color="#222222" face="georgia"><span style="white-space: pre-wrap;"><br /></span></font></div><p style="background-color: white; line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; padding: 0pt 0pt 18pt; text-align: justify;"><span style="background-color: transparent; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">This is intriguing since it goes on to show that despite having weak organic building blocks like seen in those of grapefruits, and mother of Pearl go onto protecting what’s inside them.</font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">The material can have a multitude of applications in a wide spectrum of industries ranging from security to safety industries such as bike locks, lightweight armour etcetera.</font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia"><br /></font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia">To know how Coca-Cola led a revolution, click <a href="https://patataeater.blogspot.com/2020/07/coca-cola-unveils-feature-soft-drinks.html" target="_blank">here</a>.</font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia; white-space: pre-wrap;"><font size="2"><br /></font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: georgia; white-space: pre-wrap;"><font size="2">Bibliography:</font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><font face="georgia" size="2"><br /></font></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><a href="https://www.nature.com/articles/s41598-020-65976-0#Abs1"><font size="2">https://www.nature.com/articles/s41598-020-65976-0#Abs1</font></a></span></p><p style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><a href="https://scitechdaily.com/proteus-technology-new-material-is-strong-light-and-non-cuttable/"><font size="2">https://scitechdaily.com/proteus-technology-new-material-is-strong-light-and-non-cuttable/</font></a></p><font face="georgia" size="2"><div style="text-align: justify;"><br /></div><div style="text-align: justify;"><br /></div></font><div style="text-align: justify;"><font size="2"><br /></font></div></span><br />Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com1tag:blogger.com,1999:blog-8707052728183762633.post-82274339515344850802020-07-18T05:37:00.000-07:002020-07-20T09:01:32.722-07:00Want to Make Sense of Hieroglyphs? Try out Google’s Fabricius<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: "georgia" , "times new roman" , serif;">Have you ever imagined how people at the time of ancient Egypt used to communicate with each other? They simply used drawings, signs and symbols as the form of written communication famously known as “Egyptian Hieroglyphs” one of the oldest forms of writing! Not everyone could write these hieroglyphs only scribes (writers, sons of wealthy Egyptians) were trained to do so. Even after 5000 years, this languages remains a mystery for those who haven’t studied it. To solve this conundrum Google has launched an AI based tool <b><u><a href="https://blog.google/outreach-initiatives/arts-culture/unravel-symbols-ancient-egypt/" target="_blank">Fabricius</a></u></b>, that will not only help decipher ancient hieroglyphs but it will also help people to make sense and learn about the ancient Egypt’s language.</span><br />
<span style="font-family: "georgia" , "times new roman" , serif;">To
celebrate 221</span><sup style="font-family: georgia, "times new roman", serif;">st</sup><span style="font-family: "georgia" , "times new roman" , serif;"> anniversary (July 15</span><sup style="font-family: georgia, "times new roman", serif;">th</sup><span style="font-family: "georgia" , "times new roman" , serif;"> , 2020) of </span><span class="MsoHyperlink" style="font-family: "georgia" , "times new roman" , serif;"><span style="color: windowtext;"><a href="https://artsandculture.google.com/story/OQVRt6m8dEG4rw" target="_blank"><span style="color: windowtext;">Rosetta stone</span></a></span></span><span style="font-family: "georgia" , "times new roman" , serif;"> (a slab of
stone from 196BC, has message carved into it in three different types of script
that h</span><span style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial; font-family: "georgia" , "times new roman" , serif;">elped scholars to crack the code of
hieroglyphs) Google has launched its latest AI powered tool Fabricius, here you
can “learn”, “play” and “work” around with hieroglyphs. This is the first
digital tool for translating and deciphering hieroglyphs. </span><br />
<span style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: initial; background-repeat: initial; background-size: initial; font-family: "georgia" , "times new roman" , serif;"><br /></span>
<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-large;"><b><u>Features of Fabricius: </u></b></span><br />
<span style="font-family: "georgia" , "times new roman" , serif;"><b><u>Learn:</u></b></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">
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<span style="font-family: "georgia" , "times new roman" , serif;">With the short six easy steps and interactive tools, Fabricius virtual assistant teaches you about the basics of hieroglyph script. The tour begins with taking you back to 1922 to discover who was buried in a mysterious tomb!</span><br />
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<ul style="text-align: left;">
<li><span style="font-family: "georgia" , "times new roman" , serif;">In the first stage, with the hands on activity the let you trace the hieroglyphs (for easier study) as accurately as you can.</span></li>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh9iYkICrcq-kb8JsOUqWlGg0ixKXc3tUx-f4h8U63R9a3jREaOK4qRLgJ0SFIkJ0r_VEqMCpEDDYzJvm6ZTvhgtS4T2hV-gsGV6LOc78T6rPYFCDN5MEifgWa9D0C31sXSfTnhHwLfgkM/s1600/1.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="614" data-original-width="1600" height="244" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh9iYkICrcq-kb8JsOUqWlGg0ixKXc3tUx-f4h8U63R9a3jREaOK4qRLgJ0SFIkJ0r_VEqMCpEDDYzJvm6ZTvhgtS4T2hV-gsGV6LOc78T6rPYFCDN5MEifgWa9D0C31sXSfTnhHwLfgkM/s640/1.png" title="" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;"><span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; font-size: small; line-height: 17.12px; text-align: left;"><span class="MsoHyperlink"><span style="color: windowtext;"><span style="color: windowtext;"><a href="https://artsexperiments.withgoogle.com/fabricius/en/learn/trace/exercise/1" target="_blank"><span style="font-size: x-small;">(Image source)</span></a></span></span></span></span><span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; font-size: x-small; line-height: 13.91px; text-align: left;"> </span></td></tr>
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<li><span style="font-family: "georgia" , "times new roman" , serif;">With the introduction of second stage, tool teaches about the creation of facsimiles (i.e. creation of exact copy of hieroglyphs) to prevent damage.</span></li>
</ul>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikg1rx7itXxxts3XXWYPGNEiM8-TN5lzBbYBNBb8HgSfHGPBBAuLgLQi0jdaurPafzH-aLZxkC3dtS3OCveEyTk8wrEvbNnIP7rKzNf7unsMMOqeeYdk8bs7x_r-DXp9dwwLx614p6Uuk/s1600/2.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="615" data-original-width="1600" height="246" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikg1rx7itXxxts3XXWYPGNEiM8-TN5lzBbYBNBb8HgSfHGPBBAuLgLQi0jdaurPafzH-aLZxkC3dtS3OCveEyTk8wrEvbNnIP7rKzNf7unsMMOqeeYdk8bs7x_r-DXp9dwwLx614p6Uuk/s640/2.png" title="" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;"><a href="https://artsexperiments.withgoogle.com/fabricius/en/learn/draw/exercise/2" style="font-family: georgia, "times new roman", serif; font-size: medium; text-align: left;" target="_blank"><span style="font-size: x-small;">(Image source)</span></a></td></tr>
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<li><span style="font-family: "georgia" , "times new roman" , serif;">At stage 3, we have to identify the hieroglyph from a given list of symbols.</span></li>
</ul>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6Z8xSB4uB8GsUrx23cLhPg5DgJun_j6czklPPwQU_3XlErb4z5Bt-BjOP6UYzF6NAkxCknO157sJW1mih9KiSHsXLr0cD-AM9_3K8q9lM8iHpzOaxQl-ZheM2dCnB7OJNauEiU8wOv3w/s1600/31.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="612" data-original-width="1600" height="244" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6Z8xSB4uB8GsUrx23cLhPg5DgJun_j6czklPPwQU_3XlErb4z5Bt-BjOP6UYzF6NAkxCknO157sJW1mih9KiSHsXLr0cD-AM9_3K8q9lM8iHpzOaxQl-ZheM2dCnB7OJNauEiU8wOv3w/s640/31.png" title="" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;"><a href="https://artsexperiments.withgoogle.com/fabricius/en/learn/identify/exercise/1" style="font-family: georgia, "times new roman", serif; text-align: left;" target="_blank"><span style="font-size: x-small;">(Image source)</span></a></td></tr>
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<ul>
<li><span style="font-family: "georgia" , "times new roman" , serif;">Now in the 4th stage, we have to reconstruct the damaged hieroglyphic sign to reconstruct the name of Pharaoh.</span></li>
</ul>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEivosKt7L8R2BUPOEn8ZeRNX8Wf_DHqOwm01FA-LpsHfVS49yIcfDJVwEfQ71NApb-r0FmYOJAdYGoD4C5KfPxrJOQdJbczZticmUcJZ1QWJ3OLJFNK5mB9iYWYbD0ad1lueFCOEAEqowk/s1600/4.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="612" data-original-width="1600" height="244" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEivosKt7L8R2BUPOEn8ZeRNX8Wf_DHqOwm01FA-LpsHfVS49yIcfDJVwEfQ71NApb-r0FmYOJAdYGoD4C5KfPxrJOQdJbczZticmUcJZ1QWJ3OLJFNK5mB9iYWYbD0ad1lueFCOEAEqowk/s640/4.png" title="" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;"><span style="font-size: x-small;"><span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; line-height: 17.12px; text-align: left;"><span class="MsoHyperlink"><span style="color: windowtext;"><span style="color: windowtext;"><a href="https://artsexperiments.withgoogle.com/fabricius/en/learn/repair/exercise/1" target="_blank">(Image source)</a></span></span></span></span><span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; line-height: 17.12px; text-align: left;"> </span></span></td></tr>
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<ul style="text-align: left;">
<li><span style="font-family: "georgia" , "times new roman" , serif;">Stage 5 teaches about the order in which they are meant to be read.</span></li>
</ul>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWDOHRpd44G_ys4WiATxhOvjmmcNsZNSisPNGy9iKCqarhov1FSA02TTjzUwo8toRIiZmrOrDa5kKIAIIAtMLeh3dnF913vw-XcV_KBY53emVykmJGgod9yXcyVZMa9T2RIl683MDsa3U/s1600/5.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="614" data-original-width="1600" height="244" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWDOHRpd44G_ys4WiATxhOvjmmcNsZNSisPNGy9iKCqarhov1FSA02TTjzUwo8toRIiZmrOrDa5kKIAIIAtMLeh3dnF913vw-XcV_KBY53emVykmJGgod9yXcyVZMa9T2RIl683MDsa3U/s640/5.png" title="" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;"><span style="font-size: x-small;"><span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; line-height: 17.12px; text-align: left;"><span class="MsoHyperlink"><span style="color: windowtext;"><span style="color: windowtext;"><a href="https://artsexperiments.withgoogle.com/fabricius/en/learn/read-order/exercise/1" target="_blank">(Image source)</a></span></span></span></span><span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; line-height: 17.12px; text-align: left;"> </span></span></td></tr>
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<ul style="text-align: left;">
<li><span style="font-family: "georgia" , "times new roman" , serif;">With the final stage 6, this tool enables us to translate a cartouche (the series of hieroglyphic symbols inside an oval frame). And you will discover Tutankhamen’s tomb!</span></li>
</ul>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhO3wbDKmhZl5fo_B1ACE9bTb9aQy3TqlVYQcMYZ3DExmnWj1_0enN6RkGsPx5jArCEgoxlZKfQPuBKrgcoheximhEW52Gc4dpyy5BoIiAVtdjeV_Y8kPY2D45Eb9ax0G5ZBd6o7XxFZAM/s1600/6.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="612" data-original-width="1600" height="244" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhO3wbDKmhZl5fo_B1ACE9bTb9aQy3TqlVYQcMYZ3DExmnWj1_0enN6RkGsPx5jArCEgoxlZKfQPuBKrgcoheximhEW52Gc4dpyy5BoIiAVtdjeV_Y8kPY2D45Eb9ax0G5ZBd6o7XxFZAM/s640/6.png" title="" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: justify;"><a href="https://artsexperiments.withgoogle.com/fabricius/en/learn/translate/exercise/1" style="font-family: georgia, "times new roman", serif; text-align: left;" target="_blank"><span style="font-size: x-small;">(Image source)</span></a></td></tr>
</tbody></table>
<span style="font-family: "georgia" , "times new roman" , serif;">This tool also
provides several interesting facts about hieroglyphs and ancient Egypt between
different steps of learning.</span><br />
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br /></span></div>
<div style="text-align: left;">
<b><u><span style="font-family: "georgia" , "times new roman" , serif;">Play:</span></u></b></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">Here, you can translate your own messages and emoji’s into hieroglyphs and see how they would have looked in like in the ancient Egyptians language. Casual consumers can here write unique words in hieroglyphs and can share them with their friends over social media. This way user can imagine how Egyptian hieroglyphs are equivalent to emoji’s. However, Google has stated that this is just for fun purpose and the results might not be academically correct.</span></div>
</div>
<div class="separator" style="clear: both; text-align: center;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: small;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjmcJ_7Gm9rFb_fYIqdL64MdQ2qQxjda9kmySgdo-zoQLlfEKmunE7dUKeeA9rtxhIFInfzzR6UdYPG3jm1q-v5jR8rbSiy4_ONIh78soYWEhY_aZjm5kJk8xwYTAsuSLxCp8uYwnF721I/s1600/7.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img alt="translate hieroglyphs with google fabricius" border="0" data-original-height="748" data-original-width="1600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjmcJ_7Gm9rFb_fYIqdL64MdQ2qQxjda9kmySgdo-zoQLlfEKmunE7dUKeeA9rtxhIFInfzzR6UdYPG3jm1q-v5jR8rbSiy4_ONIh78soYWEhY_aZjm5kJk8xwYTAsuSLxCp8uYwnF721I/s1600/7.png" title="" /></a></span></span></div>
<span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; font-size: x-small; text-align: left;"><a href="https://artsexperiments.withgoogle.com/fabricius/en" style="font-family: georgia, "times new roman", serif; text-align: justify;" target="_blank">(Image source)</a></span><br />
<br />
<span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; text-align: left;"><b><u>Work:</u></b> </span><br />
<span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; text-align: left;">With the help of various academics around the world Google has developed set of tools powered by <a href="https://cloud.google.com/automl" target="_blank">Google’s Machine Learning</a> technology to support academic research, increase accuracy in translation and translation of middle Egyptian hieroglyphs. This artificially intelligent tool will ease the research process, for which,“experts had to dig manually through books upon books to translate and decipher the ancient language--a process that has remained virtually unchanged for over a century.” According to Chance Coughenour, Google Arts and Culture program manager.</span><br />
<span style="background-attachment: scroll; background-image: none; background-position: 0% 0%; background-repeat: repeat; font-family: "georgia" , "times new roman" , serif; text-align: left;"><br /></span>
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; text-align: left;">According to <span class="MsoHyperlink"><span style="color: windowtext;"><a href="https://www.bbc.com/news/technology-53420320" target="_blank"><span style="color: windowtext;">BBC report</span></a></span></span>, “here in this
application users can upload the real life images from artefacts and walls
where hieroglyphs are available. These images then can be digitally enhanced to
better analyse the symbols.</span><span style="text-align: left;"> Users can trace the outlines of
hieroglyphs, which the software then tries to match up with similar symbols in
its database - allowing them to search for different meanings and attempt to
decipher findings.”</span></span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="MsoNormal" style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; line-height: normal; margin: 13.5pt 0cm 0.0001pt; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;">This
software also provides a solution for broken text where researchers can
annotate and edit faded symbols that are available in Workbench. </span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="MsoNormal" style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; line-height: normal; margin: 13.5pt 0cm 0.0001pt; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;">However, this
workbench is only developed for desktop only and it is currently not available
for mobile devices. This tool is released as an open source as to support the
better understanding and future development in the field of study of ancient
languages.</span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="MsoNormal" style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; line-height: normal; margin: 13.5pt 0cm 0.0001pt; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: medium;"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-large;"><u><b>What is the
technology behind Fabricius?</b></u></span></span></span></div>
<div class="MsoNormal" style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; line-height: normal; margin: 13.5pt 0cm 0.0001pt; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;">The main underlying
technology behind this software is machine learning and artificial
intelligence. To be more precise Google’s AutoML technology and AutoML vision
was used to develop a machine learning technology to decode the scripts of
hieroglyphs.</span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="MsoNormal" style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; line-height: normal; margin: 13.5pt 0cm 0.0001pt; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;">The translation
process of hieroglyphs works on three phases- </span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<ul style="text-align: left;">
<li><span style="font-family: "georgia" , "times new roman" , serif;"><u>Extraction-</u> extracting the hieroglyphs from the
uploaded images of artefacts and walls and creating workable facsimiles.</span></li>
<li><span style="font-family: "georgia" , "times new roman" , serif;"><u>Classification:</u> now comes the training part of the
neural network for the development of machine learning model to correctly
identify over 1000 hieroglyphics.</span></li>
<li><span style="font-family: "georgia" , "times new roman" , serif;"><u>Translation-</u> the final step involves matching workable facsimiles sequences and blocks of text to available dictionaries and published translations to achieve the final result.</span></li>
</ul>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">This
software is being released as an open source platform to improve and increase
the both the data available and functionality of the tools.</span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div style="text-align: left;">
</div>
</div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;"><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="506" src="https://www.youtube.com/embed/aJ4T4pausic" width="900"></iframe><br /></span></div>
<div class="MsoNormal" style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial; line-height: normal; margin: 13.5pt 0cm 0.0001pt; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;">The
aim of this experiment is to increase the awareness and preserve the history
and culture of ancient Egypt civilization along with the purpose of increasing
the efficiency of translation with the help of machine learning. This project
began with the Ubisoft’s research project <span class="MsoHyperlink"><a href="https://hieroglyphicsinitiative.ubisoft.com/en-GB/finalupdate" target="_blank">“TheHieroglyphics Initiative”</a></span> (developed by Google) to understand the
written language of Pharaohs.</span></div>
</div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="MsoNormal" style="background: none 0% 0% repeat scroll white; line-height: normal; margin: 13.5pt 0cm 0.0001pt; text-align: left; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background-attachment: scroll; background-clip: initial; background-image: none; background-origin: initial; background-position: 0% 0%; background-repeat: repeat; background-size: initial;">Fabricius is named
after the father of epigraphy, the study of ancient inscriptions.</span> </span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="MsoNormal" style="background: none 0% 0% repeat scroll white; line-height: normal; margin: 13.5pt 0cm 0.0001pt; text-align: left; vertical-align: baseline;">
<span style="font-family: "georgia" , "times new roman" , serif;">Available
in English and Arabic, this software can be accessed free on Google’s Art &
Culture app for mobile devices and on <span class="MsoHyperlink"><a href="https://artsexperiments.withgoogle.com/fabricius/en" target="_blank">desktop as well</a></span>.
However, workbench feature for research is only available for desktop. Company
believes that Egyptian hieroglyphs is just a starting step for Fabricius, soon
this technique will be applied to other ancient languages also!</span></div>
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif;">
</span></div>
<div class="c-pagecopy" style="margin: 0cm 0cm 0.0001pt; text-align: left;">
<br />
<div style="text-align: left;">
<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">Bibliography:</span></div>
<div style="text-align: left;">
<a href="https://blog.google/outreach-initiatives/arts-culture/unravel-symbols-ancient-egypt/" target="_blank"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">https://blog.google/outreach-initiatives/arts-culture/unravel-symbols-ancient-egypt/</span></a></div>
<div style="text-align: left;">
<a href="https://www.bbc.com/news/technology-53420320" target="_blank"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">https://www.bbc.com/news/technology-53420320</span></a></div>
<div style="text-align: left;">
<a href="https://www.arabnews.com/node/1705051/lifestyle" target="_blank"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">https://www.arabnews.com/node/1705051/lifestyle</span></a></div>
<div style="text-align: left;">
<a href="https://hieroglyphicsinitiative.ubisoft.com/en-GB/finalupdate" target="_blank"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">https://hieroglyphicsinitiative.ubisoft.com/en-GB/finalupdate</span></a></div>
<div style="text-align: left;">
<a href="https://artsexperiments.withgoogle.com/fabricius/en" target="_blank"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">https://artsexperiments.withgoogle.com/fabricius/en</span></a></div>
<div style="text-align: left;">
<a href="https://egyptianstreets.com/2020/07/16/want-to-try-your-hand-at-hieroglyphs-google-just-created-a-translator/" target="_blank"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">https://egyptianstreets.com/2020/07/16/want-to-try-your-hand-at-hieroglyphs-google-just-created-a-translator/</span></a></div>
</div>
<div style="text-align: left;">
</div>
</div>
Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com4tag:blogger.com,1999:blog-8707052728183762633.post-76646955284973723682020-07-16T07:27:00.000-07:002020-07-20T09:01:24.154-07:00Coca-Cola Unveils the Future of Soft Drinks<div dir="ltr" style="text-align: left;" trbidi="on">
<div style="text-align: justify;">
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img alt=" person holding Coca-Cola can" height="425" src="https://c1.wallpaperflare.com/preview/80/941/300/coke-coca-cola-can-drink.jpg" style="margin-left: auto; margin-right: auto;" title="" width="640" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><div style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">(<b>Image Source: </b><a href="https://www.wallpaperflare.com/person-holding-coca-cola-can-coca-cola-classic-vanilla-can-coke-wallpaper-zuyth">https://www.wallpaperflare.com/person-holding-coca-cola-can-coca-cola-classic-vanilla-can-coke-wallpaper-zuyth</a>)</span></div>
</td></tr>
</tbody></table>
</div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">While some run against time to make a corona-virus free world,
Coca – Cola innovates to make peace and enjoy your drinks in a pandemic struck world.
<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">The latest offering comes in the form of a smartphone
controlled contact-less fountain experience for its customers. <o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">The latest update is part of the Coca-Cola freestyle; a
touch-screen fountain experience introduced by the company in 2009.<span style="mso-spacerun: yes;"> </span><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="mso-spacerun: yes;"><br /></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><img alt="Coca-cola freestyle soda dispenser " height="400" src="https://live.staticflickr.com/1702/25112868710_b17130f7eb_b.jpg" title="" width="400" /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">(<b>Image Source: </b><a href="https://www.flickr.com/photos/bluemaumau/25112868710">https://www.flickr.com/photos/bluemaumau/25112868710</a>)</span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">In simple terms, it allows anyone with a smart phone to pour
a drink or pre-mixes by holding up their smartphone camera which auto-scans
the QR code, immediately connecting
to the cloud and prompts the Coca- Cola freestyle user interface with all
available drinks.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">The Coca-Cola team built this feature developing upon the
operating system launched by the company in 2019 with a simple ideology in mind,
‘no one wants to download an app with a food-tray in their hand.’<span style="mso-spacerun: yes;"> </span><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">Studies by Civic Science have indicated that almost 60% of
the users prefer to self- pour their drinks and more than 40% feel safer while
pouring their own drink.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">Coca-Cola has also given customers multiple touch free
solutions, like hand sanitizer, disposable wipes, disposable stylus pens and
more. <o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">The feature to me seems to be super fun, easy and interactive
to use. It brings in excitement in a not so exciting-chore of filling up
drinks. The feature can also help the company gain valuable insights into user behavior,
choices whilst getting people to get up and try out the latest feature bringing
in valuable foot-fall in a declining eat-out industry. <o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;">What do you think about this, are you excited to try the latest
feature?</span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: x-small;">Bibliography :</span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: x-small;"><a href="https://www.mobilemarketer.com/news/coke-adds-contactless-tech-to-freestyle-machines-to-meet-covid-19-challenge/581491/">https://www.mobilemarketer.com/news/coke-adds-contactless-tech-to-freestyle-machines-to-meet-covid-19-challenge/581491/</a></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: x-small;"><a href="https://www.bevnet.com/news/2020/coca-cola-freestyle-unveils-contactless-pour-by-phone-technology">https://www.bevnet.com/news/2020/coca-cola-freestyle-unveils-contactless-pour-by-phone-technology</a></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: x-small;"><a href="https://www.azcentral.com/story/tech/2020/07/13/coca-cola-introduces-technology-for-contactless-soda-dispensers/5414958002/">https://www.azcentral.com/story/tech/2020/07/13/coca-cola-introduces-technology-for-contactless-soda-dispensers/5414958002/</a></span></div>
<span style="font-size: x-small;"></span></div>
Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com3tag:blogger.com,1999:blog-8707052728183762633.post-1313758657538913462020-07-15T08:20:00.001-07:002020-07-20T09:01:07.789-07:00Amazon Smart Shopping Cart: Dash Cart<div dir="ltr" style="text-align: left;" trbidi="on">
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<div class="MsoNormal" style="text-align: justify;">
<span style="line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14pt;">Recently,
Amazon unveiled its latest intelligent and unconventional technology product in
market that will automate the shopping experience of customers in the physical
stores. With the introduction of Amazon Dash Cart, company aims to make quick grocery
shopping even quicker, easy and convenient for the customer. This smart cart
will simply </span><span style="font-size: 18.6667px;">recognise</span><span style="font-size: 14pt;"> the items you will be placing in it and then identify the
charges of the products. According to Amazon, Dash cart will be first available
</span><span style="background: white; font-size: 14pt;">at the Amazon grocery store in Woodland Hills
(area of Los Angeles), California later this year.</span><span style="font-size: 14pt;"><o:p></o:p></span></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white;"><br /></span></span></span></div>
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<span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><img alt="Amazon Dash Cart" 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<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: x-small;">(<b>Image Credits:</b>
<a href="https://www.amazon.com/b?ie=UTF8&node=21289116011" target="_blank">Amazon</a>)</span><span style="font-size: 14pt;"><o:p></o:p></span></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: x-small;"><br /></span></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">This smart version of the simple grocery store cart works on
a fusion of technology that involves combination of various computer
algorithms, visual recognition sensors, weight sensors, deep learning, built-in
screen, mix of cameras and built-in scale to weigh the products. With combination
of this technology Dash Cart will be able to identify the item placed in it and
calculate the total amount of the cart. Furthermore, when the customer will
walk out through the store’s dash cart lane, visual recognition sensors will automatically
identify the cart and your payment will be processed through your credit card available
on your Amazon’s account (Amazon’s<a href="https://justwalkout.com/" target="_blank"> just walkout technology</a> </span><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">). Hence, this will reduce
the amount of time people spend in standing cashier’s queue and making the
shopping experience fast and convenient. Unlike, Amazon’s Go Stores technology
where they have wall mounted cameras to keep the track of virtual cart, Dash
cart has its own visual sensors available inside the cart to identify the
products. <o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">However, Amazon made it clear that Dash Cart is specifically
designed for small- to medium-sized grocery trips and fits two grocery bags and
customers can shop store’s entire grocery selection using the cart.</span> But for
some peoples two grocery bags would not be enough, so this means that store
will also have standard grocery carts with standard checkout lanes for those
who want full cart shopping or don’t want to use Dash Cart.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<u><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;">Using few simple steps Amazon’s cart can be used by the
users. When the cart is available: <o:p></o:p></span></span></u></div>
<div class="MsoListParagraphCxSpFirst" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<!--[if !supportLists]--><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;">1.<span style="font-size: 7pt; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><!--[endif]--><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Firstly
user will have to sign in to the cart using their Amazon account. There will be
a QR code available in Amazon mobile app which will be scanned on the screen of
the cart.<o:p></o:p></span></span></div>
<div class="MsoListParagraphCxSpFirst" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><br /></span></span></div>
<div class="MsoListParagraphCxSpLast" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-no-proof: yes;"><!--[if gte vml 1]><v:shape
id="Picture_x0020_1" o:spid="_x0000_i1029" type="#_x0000_t75" style='width:402pt;
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<v:imagedata src="file:///C:\Users\KARTIK~1\AppData\Local\Temp\msohtmlclip1\01\clip_image003.png"
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</v:shape><![endif]--><!--[if !vml]--><!--[endif]--></span><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><img alt="Amazon Dash Cart usage" src="data:image/png;base64,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" style="background-color: transparent; text-align: left;" title="" /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">(<b>Image Credits:</b>
<a href="https://images-na.ssl-images-amazon.com/images/I/C1vw862Es+S.mp4" target="_blank">Amazon</a>)<o:p></o:p></span></span></div>
<div class="MsoListParagraphCxSpFirst" style="text-align: justify;">
<br /></div>
<div class="MsoListParagraphCxSpMiddle" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<!--[if !supportLists]--><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;">2.<span style="font-size: 7pt; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><!--[endif]--><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">After
that, simply add your grocery bags to the cart.<o:p></o:p></span></span></div>
<div class="MsoListParagraphCxSpLast" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-no-proof: yes;"><!--[if gte vml 1]><v:shape
id="Picture_x0020_3" o:spid="_x0000_i1028" type="#_x0000_t75" style='width:396pt;
height:183.6pt;visibility:visible;mso-wrap-style:square'>
<v:imagedata src="file:///C:\Users\KARTIK~1\AppData\Local\Temp\msohtmlclip1\01\clip_image005.png"
o:title=""/>
</v:shape><![endif]--><!--[if !vml]--><!--[endif]--></span><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><img alt="Amazon Dash Cart usage" 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" style="background-color: transparent; text-align: left;" title="" /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: x-small;">(<b>Image Credits:</b> <a href="https://images-na.ssl-images-amazon.com/images/I/C1vw862Es+S.mp4" target="_blank">Amazon</a>)</span><span style="font-size: 14pt;"><o:p></o:p></span></span></span></div>
<div class="MsoListParagraphCxSpFirst" style="text-align: justify;">
<br /></div>
<div class="MsoListParagraphCxSpMiddle" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<!--[if !supportLists]--><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;">3.<span style="font-size: 7pt; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><!--[endif]--><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">To
add the item to the cart, the bar code available on the product will be scanned
(or if barcode is obstructed, vision sensors and algorithms will try to
recognize the product) and wait for the beep sound. If the light turns orange,
remove the item and try again.<o:p></o:p></span></span></div>
<div class="MsoListParagraphCxSpLast" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-no-proof: yes;"><!--[if gte vml 1]><v:shape
id="Picture_x0020_4" o:spid="_x0000_i1027" type="#_x0000_t75" style='width:405.6pt;
height:209.4pt;visibility:visible;mso-wrap-style:square'>
<v:imagedata src="file:///C:\Users\KARTIK~1\AppData\Local\Temp\msohtmlclip1\01\clip_image007.png"
o:title=""/>
</v:shape><![endif]--><!--[if !vml]--><!--[endif]--></span><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><img alt="Amazon Dash Cart usage" 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" style="background-color: transparent; text-align: left;" title="" /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: x-small;">(<b>Image Credits:</b> <a href="https://images-na.ssl-images-amazon.com/images/I/C1vw862Es+S.mp4" target="_blank">Amazon</a>)</span><span style="font-size: 14pt;"><o:p></o:p></span></span></span></div>
<div class="MsoListParagraphCxSpFirst" style="text-align: justify;">
<br /></div>
<div class="MsoListParagraphCxSpMiddle" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<!--[if !supportLists]--><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;">4.<span style="font-size: 7pt; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><!--[endif]--><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Now
if you don’t want to scan the barcode, Amazon has provided an alternative for
this- tap “Add PLU item” on digital screen, then type in the PLU number, add
the product to bag and confirm the weight.<o:p></o:p></span></span></div>
<div class="MsoListParagraphCxSpLast" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-no-proof: yes;"><!--[if gte vml 1]><v:shape
id="Picture_x0020_5" o:spid="_x0000_i1026" type="#_x0000_t75" style='width:391.2pt;
height:198.6pt;visibility:visible;mso-wrap-style:square'>
<v:imagedata src="file:///C:\Users\KARTIK~1\AppData\Local\Temp\msohtmlclip1\01\clip_image009.png"
o:title=""/>
</v:shape><![endif]--><!--[if !vml]--><!--[endif]--></span><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><img alt="Amazon Dash Cart usage" 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" style="background-color: transparent; text-align: left;" title="" /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: x-small;">(<b>Image Credits:</b> <a href="https://images-na.ssl-images-amazon.com/images/I/C1vw862Es+S.mp4" target="_blank">Amazon</a>)</span><span style="font-size: 14pt;"><o:p></o:p></span></span></span></div>
<div class="MsoListParagraphCxSpFirst" style="text-align: justify;">
<br /></div>
<div class="MsoListParagraphCxSpMiddle" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<!--[if !supportLists]--><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="mso-list: Ignore;">5.<span style="font-size: 7pt; font-stretch: normal; font-style: normal; font-variant: normal; font-weight: normal; line-height: normal;">
</span></span></span><!--[endif]--><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Last
step is payment. While using Dash Cart you can simply skip the checkout line,
you will have to go through the Dash Cart lane, return cart and go! Payment
will be done through your credit card saved on your Amazon account and receipt
will be available via email.</span></span></div>
<div class="MsoListParagraphCxSpMiddle" style="mso-list: l0 level1 lfo1; text-align: justify; text-indent: -18.0pt;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></span> <img alt="Amazon Dash Cart usage" 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kRSEmXMPInZ07XkRBlLkwp1YExUOLcldXJ83HtAJtVySlIcEmRRiPsITAkcHty7A3V1dTRqWB8B/t4AUiRAjQzl+jR/Gp9Ag5g8qaU/x6BpzTRXakdzS0kiA4lEnitde99W2ntMSabrCdIYyTR3SeJKkEUDSfEs+cWl25tM5HIZEuUZz4MAh9ZMc+V5pyQgOVGit2JsmptEpyjIYtPNMVmOOFKvpqMTrycpXlo/gPNnz+D3335DYQ0N3L9tynNX9Jv+k0A/UZ6OdklyxMdGsuqW+qT7Ryd9J3Uu3+8EKovncjcXZ0yeOBGDDAzw8tkTHp/Uy3r9+qJAgfw4dvQQ/wvSWtKPm+PfI8Mglwk3ipzihx7u7ujYpi3UChTA2jVrsnRYLw8PtGneAkXVC2HT+g1Z2rfo7MemQI6B6ZQpkxgwa9euBS8PV2buxERlsmiMGzeGrzVq1BCeHq4MBAQo8vgY2DvY4MaNa7h53QR2NhYMNAQUIUH+rDa2t7VEUIAP4mIiERsVCU93N9haWyLwnZ9UFh0OLw8X2FiZwcPNCTFRoWl7tsS85fJYbN26mcfX0iqHJ4/vcb3Q4HcMMsTs3/l54cH9u7hicgnPnj1CeGhgpqpN6jMowBf2tlbw8/FkkHj98gWuXbkMszevEBcbkQrG4YiLiUZIUCDMzV7j5g0TXLt6CW9ePeM28bJonoeNtTnc3BwZ3AgkqX+aD63H2dEWEWFBaeuRQESq4+PlhmdPH+CayUXcunkVTvbWTDuieUxUBLw9PWBnY4WIsGDExkSz0/vVqyZ4+vQRwsODIWNAD4VcFoO4mCi8ff0S10wuwdbWBsePHcFff/2JwoU1cPfOzQzBVBYdwS8UNNf7d01x5fJF7oPmmCCPhY+3O6wt38LR3obV/ooXIlcne1hZmMPPxwvRURFwdrSHnY0lwkICGWBtbSxRvVpVvl8rVy6Hi4sTfHzcQdsFOQ6iqS8cJJnKBJjmGKeMi4vDqOGGbM1LL1oRERFZNvaNa9dRsWw5VChdBtevXs2yfkVHPz4FcgxMp6aCad26dREU5J9K2UQAyZg8eSIzx8aNGjLQpiQnIDI8GMZrVqFqtarIX6AA8ufPh3/+qQSjxQuY+RIwdmjfFlWrVMb2beS0nQh/Xx/06dULJP2uW7sGJPEFvfOFfr/eqKhVAcZrVjKgECAR86Yx5syZiWLFivH4f/z5B0qWLIGa1avi0YM7PEfTm9fRvl1bFCpUCH/9/TfX7dunF96+eQnaPyRgIolYwchpzPNnT6F69WroqtOFw5xpFi+OfPn+hlaFCli+1IjHTU5KhIXZW3Ts0B50XU1VFSoF8qNs2TKYNmUCgoPeYeeObdymQ/t2cLC3RkpSAq991swZqKilhVEjhiIsJCBNUpYk0jgcO3IINapXQ5HCGlBRKQDVgiqoWaM69u/ZxXUT5XGYP3cOqlSpjCmTpsBwuCFKlCgBFRUVlClTGosWzuM5kvaAQMzIaBHKly+H/Pn+RvmyZVCnZg389ttvKFq0KO7cufEJmJIUnZwgw80bV9G2TSsUKqSOggVVULpUKcyYMRUR4cGsqqX7Xa1qVezb8y/T+rrJZdStUxuNGzVg4LW3tUa7Nq257KrJFdy9cwv16tbBH7//zveLwJzoNW/+HAZ/uq+K+5Cjn6zmFZJpTrJLsuItrlEY1atUwZPHj797aPJdVfivpiQnY+a06ZyejVK1eXt5fXe/ouHPR4EcB9MyZcpg65ZNOHrkIA4d3IdDh/ajfYf2zBybNG4Ib083frx3bN2M31OZZosWzdGmbRuu88sv/8O6tasQFRkG3W46XNa7Vw+kJCfi+dMnKKiiwmUEtCRdWZi9hoZGIS47cnhf2r4bqWoJKCZPmogiRYrwdQI8AsFWLZrBwuwlS75VKlfma1paWtDV1UWxYsX5N4Ggjyft/SqMb6T9QAL1vXt2cR3a6yMpSq9fPzRo0JDL/vrzTxw8sIeftDumN1CrZnV06tgBIw2HQ6dzZ/zyyy9c79KFs7hz5xb++OMP/n3i2BFuQ9Jo5cr/cNkSowUsrSlAhD4JKFcuX8og1a93T4w0HAZSn9NcqvzzD5wcbJm+gwcO4LI///gD9erWRbduXUH3huqVLlUSZm9e8Hi7dm1Pm0OrFs2h17snKpYvx/XoJeRjMCXVLgGphdmbtHn26dMXM2bOQKnSpbjdjh1bIE+QYeLECfybtBUvnj1Fd91u/HvcmNGQy+NZtau4n0cPH8LN61dRvXpV/Pbbr1yP5lu3bh0sX74kV8FU2jMVYJqT7NPR3h7NGjXm9GmTJ0yETEbbQd9+pAfTZ0+fom6NmgymS42MQEHwxSEo8LUUyDEwVah5iVlndjZp0gjv/L0RFhyIZk2acL2+/XohMMgfERFhGDZ8KJfVqV0DPl7u2Lt7N/+uXrUqAvz9cGDfPuTPlw+//vorypYpDVcXB1w4f5alqBrVq7JalNS2CqklNiaC+509Zzb3U6NGdVhbmbPkJJdFYqnRQi4vXboUHj66B4rfefToUfz9998MekcOHWBwJtWxwhiIwPTggb3cjqTZK1cu871wcnJA06bSmnr37oHIiBCEhwSxwRWpmukIDQlGi+bNue3aNasRGRkBHZ0u/HvShAlc5+qVi/ySoVm8GANeStL79SjW5ePlgaBAfyCZJH/g7p3bLNnn+/svPH5wj8uGDxnC/TZp1Bg21tZISUnBtm1bmXaqBQvi0YO7iI2NQudOnbheF+3OeOdH+8nAwX17uF6RIoVx9yPJlKRSJMpgtFiiHb2AhEeEc7vVq1dxX3369EJiogy+vl5okkoToj2pjls0bwYvd3qhAp49eYTixYryWIf27wVSaH/7FpfRM7Rly2aEhgYjLORd2j1V0CAnP9kiXKh5+Z5l55/0wEfjrFy+HEXUC7FalhKDK3MEBwdjyICBHOieUrFZmpkr051o+xNSIMfBVENDA4MHDcK4MWMwdvQojBk9CjWqV2cm27hxQ1ZvWlq8ZZUgMUySYhTHsWNH8dvvv0OlQD68fvEENtYWKFasKKtIH9y7jQnjx7LaT1u7MwoWLIjz589i9eqV3PfIEcNZxSntGUr7ayTJkZJn+fJlXIckJF8fTx6OGGS/vn24XLdbF8TFSvkR3V1dULOmJOmRipjAmYIYSIAaxupmBZhqltDE02ePuL+UlIQ0SYzWSXvDdDja22Lb1s2YM3smli5dktb30mVL+PqObVt5DvRyEfDOD8uXLeXfA/rr8z6hwliJwIOMeMjwSh4Xgwf372D5siVYsGAepk2bigIFCqBA/r8ZJEmK76+nx/1019VFZATNG7hw8Rz++usvEJg+e/IQnh4uqJwqmZPFtUIhduH8Gfzxx+/SnuntD9W8ZGAUFx2Bvqm0q1atGvT19aGvr4d69evxmNqdOyIiPIjHvHbtapqandS2N65TgPFEIDkBT588ZFUyvRwRgNNhYf4KJUtocj9EZzq+ZBCW3cBKxlpiz5RvRbb/UahkaSB3d3fodOrMCcEb1q//3blIoyKjMHfWbGgWLsLn1k2bQSpfcQgKfAsFchxM69SpAx9vT1bLkkoyUR6P8ePGMXMkA6SQ4Hd4/fo5SmhKDPMASSS8q5GM8+fP4c8//8Rff/6O+3dvIj4+Bjo62tx28cL5aNmiGTp2bIeDh/ZBo5AGJk6ciP799VmyOXr4IMhyNz1jJck0KUmOJUsWcx+1akvWxFQvLDQYuqlqR72+fRikyDKWjKcaN27E9adNnShZ2qbbq5Mk0z18vXjxYnj8iCTBJNA+8IyZM7m8UcMG8PXxxvNnT1CtahX8/tuv7DZUv3593reklwgCVjqcHex431RNtSAuX7qA7j2645f//YKjqVIx7ZMq1kRGWGSZu2njeqipqfJeacOG9blv6pP2Th8/uMt7r/r9+vFcdLp0QXDgOx7r7OmTEpiqFsTzp4/g4mzPe6XUdsM6sppM4j3u06dP4HcFmLIB0nvXGAL3qIhQVolTO9qDbdmyBZo0aYxmzZqiebOmWL7MiIGf7quDgz2qVpUMikgD8Pw57X8lsN8vGUPRyxKD6X4JTF+/fApNTWmP+8C+3WyxLL3MCAOkb/nH/1Hq3rl1C7Vr1oRK/vyc3Pv82bPfFF7Qw8MD06dMQckixTjq0djRo0HuMeIQFPhWCuQYmCoMkOrUqc2WnEiOZ0vRRLkszW2mccOGCArwg5urIypVqsTMfvHCBWlrWrduLZdpFi+KNy/JVQJYuWo5M9uqVaqCwGv2nJlwsLfFP5UqoVzZcihdujQb6zjY2bAUqQAe+pTANB6LU1WSNWvWgJ+vZHRAVsajRo3g8Zo2bsiGTDSe2ZuXbKREQLF29XJm+ukBjYAgTTItXpz3cakdZaCgvUNqR3uD/n5+MBw+nH/36K4LDw93uLm5onXr1ly2dKkRG1CRm8j4saO5rHfPHihevDgbF7k42bNrS/r1kJRsZ23J4CvNbyVCQoJx/fpVqKoWZBU4q3mTE/EhmAYwLdOD6dPHDxAY4Iv69ery2OPGjGRpkSqeP3+GVc3vrXnfgylJ+2QBbGgora2fXl/4+HojKioMYWFBCA4KQHhYMOTx0bzPOSl13/Svv//icfr07onQEAL3FDx58jAVTH9J22d+9eJJmpp3314JYMn9Jj0dcvy7cI3h5ye3/pw5cwa1a0jJvf+poIVZ02fg5YsXiI8nd65PD9rSoLCBx48dg462NicWJ1eY4UOGCqOjT8klSr6SAjkGplNSLXZJRUrqw6SEWHZdiY+Lwdgxqa4xDerDz8cDiQkyDB4yiJkrWa0ePrQfp04eZ+MgAonevbojJMiPGe6jB/dQrKgkqZC0dOLEUcTLYqHduTO3p/pDhwxi4Eyv4mUwjY5AcnIC1qxZzXVJNUzAevzIYdC+4+nTp/Drr7+xT+XcWTNw6+YNDBkkzato0SJ4/PD+J4BG0tvBA/sY4MnYiAycTG9cw4rly6Gmps7jrF+3BnGxMdDtJhnc9NfrB28vT9y8eQMVK0ovEUuWGEmRfpCMa1cuQiV/Pp4HrWfa5InsXiKL/dDPlPxGX794xla8v9Ce4qaN8PH2xtq1a3g+ZI1Le6G0l6rXV5JMu2hrIzjwPZiSwRNZ9d6/dwspyUkYNWokz1lTszg2rjfGtatXYNBfn8uIBh+7xpABEknhR44c5r1qdXU1kAvLo0f38ODBHRw+uB83r19hVe65M6dYEi5btiw2blyfJgVv3byRH19yQ6J9WVrzgQO7uczKwgzlykqGUrq6XbFv3x7cvnUdMdHhH7kI5aCkKsD0K9lN9lUzvXETndp3gHpBVTYgql+rDkYZjsCObdtx1cQEjx4+xL27d3HyxAksWbQYul10UL5UaRT8Ox+7wZCa18+XeIo4BAW+jwI5BKbRmDRRUuVWrVqFAzNIQRsiWX1KYemIYdarUwue7i6sSnzz5jlob5HK059kmUr7gWR4Q876wQHv0LZVK65DzN3s7SumxJxZkkqV2u6n/bbkhAyZLUl+T588QrlykoUq1f/9119BbhoyWRyDIakZ089BVVUFBIikUpXFpA/CQOohCUx///03/PYb7e8WAFkgK9p3764L8gGlerRX+uefkrUuuZ6UKVsatKdMdQnUExNiWZoOeOeNzp07crmamhquXyWjJpIG36t46eWA9g6DA/0xwEACO4pQVLFiBZQsVYrV43+Sevz+LX4JGaDfn/sjq2Q2VgJw+uRxNqwiQyDTm5KPHUUbapC610nzIkva4prF8euvv0BdTQ23Ta9/4hpD8wgJCWD/YcXa//jjN/z5h+TSMmvGVDg62KRZGS9atJDvObng0BhkXPX29XO8efMSaupqXLZv7y6+r+GhQRg1QtIYKGg6aGB/yOJIxf3hvcgpCZX21+kFThy5SwFnJycsWrAQ9WvVZpVtwXz5oaGmjrKaJfFP+QqoWKYcShYpikIFVKCWLz9Ka5ZAz266OHv6dKZSbO6uSIz+X6JAtoMp7TVHzLEAACAASURBVGfJYsM57Nwyo8XYv3cPM3xifIogBAQOZCxz6MBeBAf6IT4uEkkJMbC3scTqVSswcKABDAbo896mmdlrdv+gfqk9SWcU0GH50iU4uH8vKNgCxW598+o51qxagc3rjeHu6pipkQqpJQkU790xxcIF8zB50gSsN14NV2d7JCfFc3/kvkMqX1JZTpo0AZcvnuWx0xv/KBh3+j1TcqPZuGE9Gw2NHGmI9TQXN4rwFMdrJMDZvXsXDAwMMGjQQBw9egiXr1zEipXLWIojUKKYwaTyXLBgPoNKq1YtEfDOh18kFGOm/6SAFk4ONli4cB569e6JSZMn4No1E5aWjY3XwMXFHglyGa5euYSlSxfj5MmjbL1M6nYbKwsYr12DjRvXwdnJjsdIksdxUAmjJYthONIQq1atwJ07pti5fRs2bVgPFyc7Dq6Rfg5kjJUgj2FjssOHD2DMmNHo168fhg0dylIqBaIglyW65+vXGcPV1ZE1BO7uLtiyeRMWL1oAUjN7e7mzxe6aVSth/vYlz4deoNxdnbBl0wZMnTwJ8+bMxO1b1xhI06vbP5xP9kqpBKbCAClvsL2kpCSYvXmLDevWQV9PD00bN0bNylVQubwWqmlVQv2atdC5XXtMmTARZ8+cgb+/wuc9b8xfzOK/S4FsB1OJqYVCHhfF/ocEJB8zOgIN2u8jIKBrZJVKEW1IeqWTrCXppOsUXvBDphmOhPhYDqAgSbvkuC9ZtaYkxoNOAuePx0z/m4BZMT4BJPUjSTkUAYiy3cjZmjcqMpT3+RRh+Gie6fuh7xKYSq4xmpqaePniOT8dvD+bQAZX0voly9soVmnT2mJI5Uxh9Ch8IIVDlEVzdKPAdz548/oFWreWpO/VK5fzmj6kwYdgQXSi6FHkfkO+tjRf6puCTNDLA9GH9jXpN+1dxqRGDyIpm8qIFrR+6T6Ec1mCPI5pQPMnelAdqpuZNEhrSk+7yMgwxEZTeMh4VlFLoR/jeF7SS4l0zxTzjKWXpRgam54BGQfZUNCa1kf1qB2BKwG34lpufNJaBZjmAhNMb9qbwfDR0dFwc3XF82fPcPf2bTy4dw/mb9+yvUJm+6kZdCOKBAW+igI5BKbESKUwdxIz/5D5f+4a1SemSWdGbYl5vm//Xu35vuzro+LIYqXA+x+PQ30p5qCQqDNj2gSmiqANpAalMIR0EKB/3C/1QX6Zir5pHMW8CQApylLXLtocRIFUmlWrVIGdtRUDWWbjK8ppngRmCrBT9Ku4rvhNn+/LpJjHGc1TlnofFNfoU/Fd0T6jT+pfsb7091Ax/sd9KPpVzOvj34oxqJzW9qX7oaifnZ9CMv0qXpM1lb4AoFkziOhFUODbKZBjYJqdzCwv9U3hBK9dvYzmzZuhu25XmL99heREeRpgfe1cSfKzNHvLwSuKFimM1q1a4KrJZZA6liS8r+1H1PvwxS076CHA9NsZT1a0iI2NBUVCunLpErZu3gyjhYvYknf6lKmYNX06Fs6bD+NVq3Fo/wHcuX2HpVRqIw5BgeyggADT1GDlWcVkSaKi4PEULYj2NinLyceGQl87FvlrUv5XWysz+Pt5sjpbIbF9bR+iXg6AqVDzZgdvyrRPCrJw+eJFkE9o04aNUL50Gc7yos6GRQU4iINqvvwczUijoCpKFC2KSuXLo2XTZhg3ajROnzqFd+8k3+pMBxEXBAW+kQICTLMYTAm8SAVJe3mkZqXv3wtoBJzkQ0lSKvVHvzPap/3e/kW7rAFakkyFNe83cp7vrG5pYYGxo0azOwu5tRRWU0dlrYpo3bwFenfvjsEDBmDYkKEcZa1f797o3L4DGtWrD62y5VBYVQ1k4VtKUxO9e/SEyZUrHCL0O6cimgkKfEABAabZAKYCpLIGpP4zdBR+ph8wlez6cffOHbRt1ZqlUJJGu+t0xTpjYzx88AAe7h6g+LoR4eGIjIzktGwhISF450epGu1gevMmjNesQa/uPVC2ZCmo/J0PlStV4vZkqCQOQQFlKSDAVIDpd0vO/xmwy+Z7LPZMlWVDX25PGV1aNGmGImrqqFSuPO+Nen1HirTAwAAcPXIEHdq0hYaqKkoXK45lRktAOVLFISigDAUEmGYzoxWA8+NLqcI1RhkW9OW2FMWrl253aGoUhlaZsqzW7dKxE2bNmIFHDx5+uYMMapDh0uD+Bqz6LVOqFPbv359BLVEkKPD1FBBgKsBUSKZKPgNCMv16hvOtNRMTE7FsyRKUKlYc5UuU5M8SxYpBQ1WNDYwo/+ixI0e/a+/Tw80NvXr0gGqBAhzcwVykXfvW2yPqp6NAroIpOeUr/CCzSoJT+CVmRX/K9kUGQ1m9vvTryu7+048lvmcuYQswTcdRsvjrq5evUL92HZQoUhQVypRF3169sX7dOqxavgItmzTlOLw1qlaFqanpd41MMXvr1qwFdZWCmD1z5jdlnPmuAUWjH5YCOQamZI1KKczopIAElPOSwIoc+SnaETFrUpcRQCQnydgS9mssVz8GFMmR//staNODBs2PYr6mL/uW7zQ3aX3vAyN8S3tF3cxAXdE/1aNxKHoSlX0N3RR9i8/MQfJraSPUvNnHH3ds3YaSRYsxmI4dOQq+Pj5pgz178hQtmzWDSr586NO7NwICpIQNaRW+4gtlkFm6aDGrewlUzd+afUUrUUVQ4FMK5ACYUri/GPj5eOLcmZM4sH83Ht6/zRljKKH3ju1bEB4amBbNJjToHc6fPQULs1cMEAqGRiCh+J7+k8rpVADzqZNHcfL4kUzdUhT10/eR/jsBO/WVEB8NkysXcPTwAe4/Iwkzs76onELe2dlYch5Q8hUlN5n049B3qvdx2ce/KQtLRuNQCD1KR7Zty0aEBgfA0d6G6Usp7NLPNaMxuD8lVZsfz/Nn/i1JpjGf/neJEqUokJSYhPlz5rJKt3LFijC9QYnoPzwOHTyIUiVKoEyJUjh/7tyHF7/yl7mZGRrUqctB8Tdu2PCVrUQ1QYEPKZDNYEpAGgdPd1cMGzwI2p07YdAgA0wYNxr+fl7YuWM7OrRrh6AAf0lqRSIHO5g0YRwunDvF2UikuLIylmYpTqsCHEj6ot8UxH7r5vUMyEAyRo0cjrFjRqWuMolTvRGjp3ZUn/qjjDMkyX0MACw9J8mBlETO6jJ//lwMHToEyVQG6ut9XGCKUUvxYak/uSyKpUEKmC/1L+VRvHPrBhrWrwtL89fcnvxFaR4KKVKxNgJvxfwICGltNBdFHNxrJhdx7swJ7oPq0rxpjsePHkTrls0R4O+LB/fuYMK4sZy8nKR/GotOiqOriAfMYyTGsQRLUqxirI/pIH5/o7QaKcVwlpLYf/gP9kP/yubQfmRhO2n8BA7C0LxJE9jY2HxCTj8/P85JSkEa5s6ahcSEhE/qfKlAFheHUYaGnGB8gIEBYmKEq8yXaCauf0qBbAVTCntHuTOPHz2MmjWqw8LcDAkJCSylyuNjsWPbVujqdEFURDj8/bxx794d+Hp74uH9u3B1doC3pztnf7l6+SJ27dwOyhgTz4HZJUmUogvt2rEV1atVxt69O+Hp4YxpUydx/tK7t29hz66deP3yOYMXBWp/++YV/t25FefPnkBIkP8HgErg5eXuguPHDuPokUPw8/HCihXLoK+vj1u3TLFn9y48e/KIJWoCZWtLM+zetQOnThxBgL83Z6qhiEWmN69hz+6dsDR/i7t3bjHYOdrbMnDdu3MDvt7uoHqXLp3nFGwP79+BPC4GEeEhePXyGXx9PFiKdXCwha2tFSiTir5+H3TV0cbt2zcQFOjLkjOB6emTx6DdqQN8vb2Ybg/u3kZYSCAc7Kw4FR2N8e+uHbAyf8t9UvD8Z88fsTbg8qXzaRoBAZ7fCJ4fS/XCz/RTzpIFJTExMRg/ZgznHO3Yvj3c3Ch14acHWfWq5s+PXj16sq/ppzW+XLJ96zZoqKujWdNmcHZ2/nIDUUNQ4CMKZCuYUhi9lKQEDvZet3ZtLFq4APb2dixh0Ty2btmMfn16w87GBoMHDcSC+XPg7uYKA/1+uHzxHINwowb1MWvGNAwcYIB27drg5aunnOczMT6Gk4wPGTSAk2EPHtQfD+6ZYt7cWahduyYWL5iPnt27o03rlvD2dMXjxw/Qq1d3zJszA316dcfypYt4rzYuJpIlN2dHO+j17Q3dbjqYOHEcAyc5hFevXh3z5s5F//76HG+X6lmav4F+v96YM2s6DPT6YsH82SBVH6UKo/FGGA7Fwf17OCl4547tYWVpDqNFCzBkUH+4uThi5Yql6NixPSZOGIu2rVvg9Inj8PL0QNeuXXD9GiXOBpYtM8LkKZNw/cZV1KheDdWqV8X8+XPg4ebEwCiB6XF06dyRk3vfuGYCg/568PbywOxZM9CubRssWjgfOjra0O3WFWEhAbh10wS9euli/oI56NFDF1s2reO+SGIVgKoEoDKYCjXvR7xF6Z8UR3fC2LEMpu3btIWLC+U6/vTYu3sPNAsXQZ3atWFpZfVpha8ouXHtOsqVKo0q/1TGw+90t/mKYUSVH5gC2Q6mcTFRiIuOwoVzZ9CtaxfUr18Hc+fMRGR4COcfbdqoMfT69cP06VMQEhyIwAA/tG3bGqdOHsO/O7eDwCgkKBAx0ZHQ7doF8+fPZvUlqTtJ/XruzGm0btEczg52/HvSpHEYNNAACfJ4vH79Cg0a1APtzU6dMhn9+vXmXJ+rVyxD65ZN4OMl5RYl6XnDujUMhFK+0RiOg7t0iRH69umDmOgouLg4oUXzZiDV7VKjhejeTRu21ubYuX0L2rdtg2tXLqNZ0yY4c/oES6mkEqa94eZNG2JAfz0MHTQQri6OsLJ4i0YN6+OqyUV+rNasXolePXRZkmzfvh2uXDrP5fPmzsbw4YMhk8Vg9KiRmDp1Mn9XAB9S5Dh98ii6dO6A0KBAXDx/Fp06dYCHuxtGjx6FCePHISU5EQ8e3OWXAHOz1xg1YiifTk52oP51tDuxJJuRyluA6zeA68+q5s1mxiiTyTB10mR2gWnWqDGsMgHKl8+eo1bVaihRrDiOHT36XbN6+/o1alatxgnDv3fv9bsGFo1+GApkM5hKuUnJ+IYOygd64fwZlhz3/LuT1ZRqaqooWbIEHj6UUpX5+noyKJw5dQK7d27HwAH9IZdJ0Ukmjh+D0SMNkRgfC8pBSunOLl+6gHZtWsHf14vHmDB+LCZPHMffbawt0a5ta5jeMMHQIUPQtk1rGC2ehwnjRmGp0QJORE7qXdpbJPWw4bAhqVIz7ZkmYvHiRRg5cgRoL9bHyx1dtLVBicwnTRiLFs0aYdGC2Zg4fjQox+jJ4yfQpEkjODq839chtbCWVnmoFlThFwOaFCUyb9a0ESffpt/XTEzQuWMHPHpwj/u/cd2E577UaBFGjxzGuU3HjRuDuXNmcTlJ0gR0tC96+sQR6Gh3QHhoMC5fPI+uOl0YTMeNHYOlSxZz/Vcvn6JTp4548uQRevfURTedTjBaNA/jxoyE8ZqVHISfXiYUe7ECRL8BRBXq3p9VzZvNe6a0/zl35ixoqKiieuUquHf3Hj/TH/8JCw1FT93uUCuggskTJoIShH/rYW9nx0Hzi2sUxsEDB761uagvKIBsB1OSpAICfODt5c6JsIOCA9C5cyeQRHZg379o374tZs6chm5dO8PK4g0CA/3Rrm0rkFXu3t270KlDO3h7eeKdvzfvD65etYINf8jqlgDl0sXzaNSwAatSSRocN2YEgxDdWwvz12jWpCEe3DXF9GlTMWjgQDZwCg3y531OMvSh+SE5AZs2rEPL5k3hYG+LmMhwyGKiWE1K6mcyFCL1KoHxbdPrrCLu00uX92iDg/x4D/QtScH160rWv5TrMzYKd2/d5D4XLZgH7U7tcfP6VVhbmaNRowY4c+YkkpOTsNTICH179YSlhTnatm2DkyeOQi6XsRHV8KGD2FhozOiRLJ1GR0rqWLLwpbXTfm3rls3g5+vNkilJ8aQmHzVyJObPncOP97OnD9C6VUuYvX3NxlkTJoxGUJAfggN9+WUiJDiA1dbBgX4CUBXg+K2fPyuYZjMDZbeVxUYoVkgDJYsVw+aNmzIckeqtWLacfU5bNW+eqTo4w8apha4uLmjXshWHK9y1fcfnqoprggIZUiDbwZRcRF68eMxWvEOHDoaurg50u3eFo6MNjh45gGFDB+PdOz/MnjUNQwcbMGMfaKCHy5fO4fDBvaikVQFjRhqiT68evL/q5GDLlq4kQZEhEBnpkORF/d68YYJFC+ZgzqxpLE3aWJujW5eOeP38Cd68fgWdLtoYNmQQS5M7tm1CZHgwAwhZu9JeJqljaQ9ypOEw3L1tis0bN2DypIksuXp7uqB3rx54+OAO7O0s0bO7DgYN0MOkCWOweaMxIsOCWFXcqkUzLt+xfRPu3DZF39694OHmigP79qCrdke8fP4EGzeuQ4cO7TDScCg6tGuN8+dOIy42GqNGj0Srls0we+Y0aGt3xKyZ09hql/Zf69Wrw3u5bi72vH4C+JfPH6NF86bYuX0bTC5dwECD/rz3OnPGdKxeuQJkXfr61VN0764LJ0c73L9/Gx07toWh4RBMGD8ap04cxZPHD3ldpLImegrJVEimGXKKXCpcv9YYJYsWRbHChdmXlILZZ3Tcu3MHlctrobSmJo5+h6rX08MDndu1R6GCqti8cWNGQ4gyQYHPUiDbwZTUh2Q5S+C0b89unDh2GO5uDuyy4u3lBltrS8THxSA8LAgvnj2Cl4cLHOxsEB4ahE0bjNFNRxu3bt7AlcsX2cWG1LLpGX5cbCTsbC1x5dIFODvaws3FgT/J7SM0+B1Lu+R7mZggh52dDY4dOYQTxw/DxsqMpVJFX5TqjFTFly+exYVzp+Hp7gIvDzc42tsx4EaEBXEbstwltbCLsx1OHD/E7ikWZi/ZMpjW+vjhXZw8fhhmb1/w/q+NlSWiIiioQjRev3wGdxcHxMZE4O7tm2yk9ObVM16/PD4Obu4uLJHfvnkNTva2vMdK66CxH9y7hVum15iWNI5i79TszSuYv30Nfx9v2Nta8140GUm5uTjznIKD/GFpaY7QkCBOLG5paYbDhw7gzKnjbDm8a8c29O7VHUGBfmyMpKCH+PwGUBWS6WeZjDIXd+/cyQBJadMqV66M+/fvZ9hdUEAA+vToiQJ//Y2RI0bgW5OAe3t5QadTZ6irqGC9sXGGY4hCQYHPUSDbwZSYMjF/8skE+Wsmx3NAhJioUMjiotJ8IMkIhiQj2hMkdS3tU27asBYG+n0RGxPFUtbHQEp9k8ozkf1H5RytSB4fzaCgUOGSZEzjU90EeSz7XZJUl1FftLdL1+gkEKPfinoEXh/2FZNWV1GH2rBvZ7Kc29G4iXLyLZWiPdH6aJ0SPWRMC2nNERz9idyFkCxHcoKM6ZIgi2GfU6qfkuo3qgBRBV2JVnJZDMjHleZH12k+VEYWxjwH9s+NQEx0BNOK7wPfCzkunD3FltO01vR9U//i/DoaiHCCn2Mxyl07fvQoypYqhXIlS0GzaFEsXLAApNbN6Ni6cROraWtVq47nz55lVCXTMm8vb3TtrI1CBQsKMM2USuLC5yiQI2D6PUyZgIn8JZ8/fZgGKN/Tj2jzeUCQXjreS7qCXp+nV0b0EWD6ORaj3LVLFy+iYrnyDKYU7L51i5ZwcnTKsFMrC0s0rt+AVbVki5CcnJxhvYwKFZIpqXm3bMp4bzajdqJMUEBBgTwLpsTkSVoS+3jfztwzYviZlZHkSmdm10X5l+kvwFTBTrL+8/atW6heuTLKaJZgQC1ToiS2b9uW4UCUYWbmtOkctL55k6ZwcHDIsF5GhbRnqt2hI7edNGEiKCqSOAQFvoUCeRZM8yITJ4DPi/MSc/oy4GUnjUSg+29hOd9Wl5KCUwB6UvNWq1SZA95309EBhRHM6Hj86BG70RQtpIG1a9akVclYMZx2GW6urujYrh2KqhfCP1oVsXv37vcXxTdBga+gQJ4CU8U+JTG+9N8/xwhJeqW6n6uTlddovG+R5GgfMjf3I7+VPjRf2oemPW1l6UZ0ovGV7SevtxeS6Vdwmu+sYmlugSb1G6BiuXIw6KeHWlWqoVRxTRw+eCjDHkmiJF9T9QIqaNGkaVo83y+BqbOTE9q1ao1SlKGmaDHUqF4d589fyHAMUSgokBEF8gyYEhOn4AMhQe+Y+VImmZBg/08YscSgpSD1JCmSpTDVVaRxy07GK43nxy41nxuPjIxksbQPGcbWtUEB73jf91vnRipuNkhKImMlOZJSDYy+th+aL/mPSvTJPGQg0T5tLA6OTwZYlMot5BP6ZzQ2rZfO9Nf4foYFgayJo6Mkf97013+o78KaNyPekiVlLk5OaNu8BScF37xhA0YMGcpA2U2nK/z9/TMc48mjxxwRqZBKQSyYO4/jgWdYMV2hna0tWjRugjLFS6Bi2XIorF4I9evVg6nprXS1xFdBgcwpkONgSkxWwUgJGBVSXlKijN01FsybBXJ3oeD4FKknKjIkTfKkthRjNsDfE/FxkQwSFIv37JkTaRa4ir4Vn+nHo+8kxX5cpqgrfaafHwXUJykthC2QyW1n8sQxeHjvFlKSE9Lmnr499R34zodfCijc4YtnTzB29Ci4ONmxO5BivYo29JuAV/GbPqmMgND0xjVs2rQeGzeuxf59u9ldRmGxq6iX0XoUUj2B6Lx5s9iflKyMiWbpx6HvNN/EhFhYW5lh40ZjjrS0detGeLo7QR4nSeHSi8F7uij6oLZ0EmB/HPSBctJSaMQZ0ybzNYV0TiAt0fRDyZfW/DFtqO+P16cYOy99Csk0cwaj7BVfb2/odOjERkX79uzF1SsmqFi2PFv2ZqaKpWQa82fPgUZBVVDi8Pv3Mo6clH5u1lZWaFi3LrRKl+Hg+hT8gYLnt2jaDA8fPEhfVXwXFMiQAjkApqEMFuRuQtIVubGQ+wv9JgZL/p3E6Cl834Z1q6HbtTPHoKWgA+T/SCEIFVIPpUG7ef0KhwKkvghwKBDDrp2SQUKCnNKWxTJg0CfVoZMSfNPv5ETJNYXGpj5JcqQyYtjUF7mUKNonJcQyyMjjI4EUSqmWBCdHWzRr2hAmV6S4ujR3RX1i7tQP/V61YikuXZByK968fg2NG9QH+X4CCVKfqapPkgapPqVwIxpQiERSr9JvAkK9fr3RvkM7GBkthJ5eXw708PTxfZYiqR3Nl4COUsoRENGauJ/YSM7WQ767XbvqYNsWyTqR5kvrktS40j4j1X9w/zbatGmFPn17Yfr0qZgxfSrM377iNVMbGoPq0fpoHBqP6EplFHB/x7bNOHhgDz9gCkCktVJWnQ5tW3P0KorYlJQohYGkOVC+WJoH98GuRPQsxKYZnNHaFGPSJ70I0D3KSyCqmIsA0wx5S5YUhgYFs/+oav4C7LISERGBYYOHoGD+/GjXri1cXV0zHMfW2hotmzaDaoECGDhgAEJDQzOspyh88/o1alSpigplyuL6tWu4evUq/yZ1cZsWLUFBIcQhKPA5CmQ7mFKwAhcne5w7e4IjHm3fuhmk9rzGMW7HYOb0KSCAoGPb5k3o16cng+l647UckUgKeBDFwOvkYMMAU7XyPzBes4rTnFHUn/FjR2O98UpMnjgW9+7c5LqmN01w/txJBmhyryGpkjLFjB1tyGnYIsJJpeyPE8cOwdHemkH+3t2boNyhBPgU2nDWjKmYP3cm9uzazsHyKQh+h/ZtOTPNyuVGmDFtEt68fg7ybY1LzVN65cpF1KhWhePtUvD561evoGWzZli/dg1nmSFp283VkSVbSoC+cMEcTJk0DlevXEgFqzDIZVLAiZ49dLFunWREER4eigEDDGBgoM9+q7TOubOmYfzYkdi7ewciI0I4ROKWLRtAsXwpZRyFGTQw6I8xo0Zgg/FqjtZEmWOkF4lwfsmICA9Cr57dMdxwOCIjw5CSLOcAEwScFChi/pyZGDdmFAf0jwgLhquzPc6ePoajh/dhz7/bQdlqmjdpjBbNmvA83vl5cf8EphT1iYJuhIUGct7Z1SuX8r1aungR3JwdQWNfPH+a78HihXMxbcoEjupEOVh9vNz43lEc5b27d7JGguakALA89SnUvJ/jMUpdi4mMwmB9AxTMlx/z583jvm6ZmqKSlhanTFu+fHmmLjB7dv2LkrQHWqw4tmza/Nl5PHvyFJW1KqJCufK4fVsCzjOnTqFOjRocaL9R/Qag38lJX+9u89kBxcUfjgLZDqYU9/beHVNU/qciBhjoY++//+Lk8eNo07IlVq5YjlkzZ6BTh/ZwdLDD7l27OGSgTBaL9euMMcCgf2r0oCiWwmxtLKGr2w21a9XE5k0bYGNlAX29vpz95NDBvQzElFkmNDgAhsMGo2njhlg4fy6rS0cYDkN//X7YtX0rA+KG9cZwdLBHm9YtOLoS3VmKETxqxHAE+PtAr28vDB5ggP17/0WrVs0wcuQweHm5o0WLZhg4QA8HD+xFhw5tOd4tMXaSsgmkzp45iSqV/0HP7t1wzeQyj025XCld287tW1G7ZjVsXL+Gc7rq6/XhVGvbt29Gp47t8PTxw1SJPYqjN/Xp3RPGa1exREvzO3XiOFq0aA5XZ0fcMr2OQwf2Yue2LWjauBFIAt6+dQs6dmiLo0cP4+LFs/D29oCeXj/OvHP4wD4OgEGxjinSE0vhFI7RxhINGzbA+VRJWqFaJQmTEgQc2LcXu3buYFpSMH3KNfuPVnkMHTwAu//dDpPLF1ny7tCuLegeUIQoooMEprvRVaczQkMCYGdjybGWT588ju7dusFo0UJ4erqhfbs2GGigjyOH9qNTp7YYOsQAkRFhHEpxzJiROHniGAPygf27WVLNUyCaGthCSKbZxxfjZfFS4u58+TF18hSQ+4tcLseUiZOhpqKC2jVq4vGjxxlOICw0DIbDhvEea50aNXE/k0D57YcO9QAAIABJREFU1PjB3Xsslf5TsSLupVMLX7l0CU0bNOQE5VW0KmH50mXw9JSSamQ4qCj8aSmQA2Aqh+mNq2jQoC5evXrJhB43ZgxGGhry9+jICHTp3Ak7d2zH/r0EiL1BYGq8bi0MDPQQFU5qYmmfk1SKa43XYNDAARxZiJhY7549sGWzFEvz0gXKINMG7q4uGDZ4IObOnoWUlGQ8enAfDerVxcsXz3nM3f/u5Bi8T58+ZtXplSuXuHzWrBmYOGE8TG/eRLMmjeFgZ8vl8+bNhKHhILi5OaJ9+zY4d/Y0l9N+JgXop8D5CanRkoKCAtCzZ3fs3fMv17l96wZaNGsMeztr/j1m1EjMnTUdVy5fQK1aNbF3324cO3YE9erWZkmO1MkEzGGhAejbuweMV69IVTMn48qli2jerCmcHR1AmTJumd7AuTOn0Kplc+zbuwcH9+9D0yaNcPDQfgQHByAqKgy9e/fiFxMa/Pq1q2jVojlL4mTMRPvUb9++RqNGDXHjhgkHz1eAFalVgwL9OTH62bNnGPQo9OCdWzfRoF4dDpxPUaooFKThsKFYuWIZry+9mpf2ebvpdEZwkC8outOzJ49x49oVzvdK8Y/pZaZjh3Y4cUxKm0WJzGnNr14+R9OmTbBgwVzO70qZcaZPlWIkUyhGxRzzyqcAU7712fKHMsBMGj+B1bqjR40CJQynw9rCEs0bNQGpfwcNGJipGtfSwoL3PakeudRQQPuMjmtXTEA+rDUpetKLFx9UefTgIXR1dFBYVQ3kctNVpytOnjyJkEziBH/JcviDzsWPH4YCOQSmJmjVsmlqerJkDCWgmzNbImJyAkuUxmvXYN+ePeiv1xdJSQlYu3YlBg/SZyknPg1M5Vi1ajkGDxrAe6wkRRGY7tyxlfu6cO48tDt2hIebC6dTowwzdNy4aoImjRrCxdlRqnf2NDq2b4P7926hU6f2ML11k8sXLJjHge1NLl9G86aN4OUu7cdQujbKBUr5SClQPAEhHZs2GaNXj66sLiYAJGmP4gB366bDuVqpzu1b19CyZZPUtSdh4vhxWDh/NsfgJTBdv94YO3Zsw47tWznGMPXDYBpCYNqdQyryYADWGa9BJ8oM4+KMGdOmYviwIVizZhXq16uNvXt2ITEhnvPA9unTCwMG9IeZ2WsMGGiQFtGFEo9TYH1Slyv2XCkGcZPGDbFzxxYehlSsBOhk+DV92mQMGjSAX2waN2qAfXv+ZTClYP7OzuQQn4zY6EgMGTQQawn0gTTjKZJM9+39Fz27d0VkZCiOHDmIHt27seq2R/cuGDtmBJwcHTig/5nTJ7ktZQkiKZVefigT0OyZ07Hn323YutkYjx/eYfBPyIPB+AWY8u3Ltj8zp09HwfwFMGTwYISHU+pF6fh35y6ULFacXVm2bs5cjXvi2HGOokRgOG70mAyB99yZs9xX/bp1YWlpqRgi7dPDwwML58/HPxW0UODvfChdoiQM9PRw5PBhuLm5ZRriMK0D8eWHp0COgOm1q5fQoH5tWFm+ZYKuM17JOUgpQDvtuTVuWB+mN6+xdEWSk6eHGzasX4t2bVrwHh2BC0k8lBCbylu1bIHXL5/Dx8sDut26YfPGddwv5UBt06olPFxdQJlnFi+S9lgc7W3RsnkzBiNi4KQCptPezgbNmzeF8TpjODg6oEcPXc6o4u7mzKA1feoklqp1unTEmFHDOYg+5TE9f1Zi/sZrVqBL5/bswqOICUwq5m5ddTBl0kR4erjD5AqliKsLWxsLlvxGGg7hvdaXL56iWbMmOH7iODw8PWBjY4XAAF9epwJMdbt14ewuFuZvcOzIfga9XTu2s2Rap3YtVr++ev2KU7pt27KR1dMEjrdMb3I6uGNHDvMeK72o0EHWtbS3SXvEZPwUFx0Biv+7aMFcNG3SGGfPnGLDo6ePH/A+dr06NbFunTEszM04O82OrVs4jVyDunWYdgS6FBN42JCB/ILkYG/De5t8r1L3TJs0agBKTzd0yCAMHKAPNzcXjB45HEMGD4S9vR2r0I8elXwGt2/dBN2u2nB1sud93GVLjODh7goHeyv4+3nD1toKV69c5MD/Cgk4L0in5Poj45y9QibhBy2L/8yfOw8FCxRAf319hIa8NySi74aDh0CtQAHUrV0bDx8+zHBkuTwey5cshaZGEQ76sGjhQsTGxX5Q99jhIyhepCgaNmiQaeQkUi+bXLmC/np6KF+6DFT+zsc+r21bt8HsWbNgYmICX1/fLAdWL09PXLtyBQf37ce2LVuxYf16zmyza+dOzqN859Zt2NrYICgwKNP94w8WK35kCwWyHUyJaT9/9hDjx4zkhNjkUuLt6crGJj10ddCtqzbIMCVeFo3Xr56jVw9dNkh58ewxevboyllZSOIj5kl9vX31nJNcDxrQH08e3cfyZUtw/uwpICWJDWbIEtXX2wMrly/BwQO7kZyUgIT4WE5E3r1bF/Tu2RUG+n3w5vUzVj1SEu1WrVtg4uTxGD58CNavW83Wqg/v38LwYYMwa9ZUlkbJcMnPxwNTJ4/Hw/u3WSqjnKvz5s5klSztE9IcyeBq355daNOqBVatWMIGUVMmT+AUb0mJcaAXia2b10MeH8PSaLduXVidPXnyBAZckhipr8iIYCxeNB/anTtgQP8+0OvbA1s3bwDlNCVJyGjRfPTu2Z2lRwImSjp++eI53sukF4lZM6eyBfGypUY4feo4kJKMp0/uY+qUifB0d2YpmoCIXgJon3PRwvmsctXu1IGNvGiNtLfbq0c3zJg+hfslICNXn3GjR8Ld1ZkTEtB86eWC2pEBEfVNLwN0r8zevESv7t2we9c2XLpwhqX4yZPGYdRIQ2xcbwwPdxdOyk7ZcGh+F8+f4vR5pHEgtyBKu0eailEjhuH50yfYsM4Yen17c0YholFeAFKaA92PeBmpHwWYZgeXWrxwEYNpn969ERT0YQo2szdv0KRBQ77eq2dPeHllvJ8ZHhaGSePGcyD8UpolsHrVKsTLSQsjHWSspFmsGCpqaWHVylUI+Yz1L1kGnz93DkMHDWajJco0o65SEBXKloNOZ22sWLoMFAbR18dH0f0Hn7TvK5ORB8Pnj5joGJw+eQpdtbVRtmQpFC9chNXMRdQLgc6iGpTnVZOl7oZ167PV89LFRrxNlf6l4/OjiKtZRYFsB1NijJQ3NDDAB1ERIeyXSOBIYGH25jmsrd4iLjYC8lQwIsDy9/XkfVL6Ttah1IeCcRITfefnCUcHazbSoeTctL9IbiXkTqIYJzjQH2HB77gtgRwxfTdXB7x88ZjBg37TXiylNzM3fwVHR1uEhQexsQy5YPh5e7CBjJ29Lef7XLxwHgMEjUdtqE/yeQ0K8E2bG82Rymm+lKLN3c2R6wYF+qatnSyIySeTJDry43S0l4L5u7jYs+tH+rVSPQ83R7aGpnEoQwytn+YdFRHMFsfurrR/GoBImntwAEiKff3yKcJC3nFdknZDQ97xvMh6VkGftHHIEpWzzESzFuDt6+fsE8sgERUGa8u3oByqRFs6+V6+e38vqR9as4e7E69ZcY+pPbnU+Pt58ksIzZt8bSlvKqXGI9pRXVpXRGgg90H9Mz2jwzhrDr10PX/2CA521vwSQVbbG9et5fuQNv+8kN1GWPNmFT/KsJ+lRkugqqKCnj16IDAw8JM6hw4cRLnSpVFYXR3Tp01DbOz7uLrpX2/8/fwwZOAgFCqgwvuja1avQXw8ub2RJ8EWlNTURKkSdJbA4MGD8fKlZOPxyYCpBeSmQ9LwMiMjdGzbDuVLlYZ6fhW2/tUqVx462tqYO3sOTE1N0wJHEKgvXrQYhsOHY+e27Xj+9BlCQkKQ8lFQfnpJGD96LMqXKcP90WfjBg3RtmUrdG7fAZ3atUerZs1Rr1ZtVCpXnoFWNV9+VodTYoD+/fRx/uxZxMV+KIFnthZRrjwFsh1MFQBDAJqeARJzTUmW0p0RmBKA0XUKIEB+lvw9VUqjPhSnopx8D4mJkxRE4ELt6Tf5L1JdKqcx0rcjaYn8I2kuVE5tFPOga9SGQJaY/OYN61kV2bdvb/50tLdhJq6QkqktjUttFGMoPmUxUio28nuV5vR+7Yp5Kear8LOkcdPTh/qiuiTNEohmSL+keJ4v1SPgovko/HkV85KupafP+7ko5kufNE+JPvFpYKXoj+am6Ofj9VBbmrc0z7gP1qC4V9SWvtNa6aTfRHcqo3XJUoNUpKcn+QDTuJKvcAJLfyQZU85akv7Tzz23v4s9U+UZ0ed6IAtatYIF0UNXFwEBAZ9UJYtfinRUrJAGKO/p5o2b0tSd6cGUGlIMXv2+/dgSuLRmCRgtWoTQkBBs3bQZJYoXh1b58iDJlbLHNGnYENu2boW3t/cnY6YvSE5Kgo+3Ny5fvIh5s2dDp3NnVNGqyPPRUFNHowYN0/Zhz58+g7IlSkItvwqKaxRGrerV0at7D57HtatXYW1piW2bNrO0TRGcaE09unZltxwXFxf4eHnhnb8/6MXA090d1lbWuHvnDiigxfQpU9GudRt2Byr4dz6UL1sWkydPhksmvriKNZCRl72NLe7evsMWz2TZrDjv372L9KeiPP3n++vv22V8/S7uUX937qb1r6gn9XGPx6f1UKCMgHfvFFP8T3zmCJjmNrP71vGJyXt5uHJC83t3bvFeJKmKv7UfUf/9S1BW0IKAN6MoTlnRtzJ9UKD7OJmQALKL4y1ftozBLzMwpXGJ8Q4xGMhSJ/mLHjsqWYhnNCdXF1cMNBjA1rklixXDhHHjMG70aBQvWhTanTph9YqVaNawMbvUUA5VUrMe2L8fPpmobdOPQblWad+UwGHyuPG8R0uASUZNtOc6fMgQBsi61WuiUd16nA2H1LfkC1ulUiU0rCOVkdq4fu06MF6zloE6/RiZfaexXZxdQJJ6N+0uKFKoEAqpqaFXr16ws6OgMRkfFubmLFlXKaeFOhWqoF75qqibdlZB/fJV0aCcdNL39CfVrV+e6kj10l/78Dv1K531M+mL6tetUAU1KlbGP+W1QHvlFM3qv3IIME0n9aZnqCQ9ScnM5RlKn+nriu9ZC5qZ0ZNecjK7lpvlQjLNXnZHkimBS8/u3RGYgWSqGN3Oxhba7TuyTyj5n5JVfmYHZZ2ZOH4Cg10xjcJspUsGSEMHD0ZSQiLM3rzFmJGjOE4vGRqR1TAlD9+wbh0szMwQ9xUp2v7dsZP3aCmOcHR0NCgDTtV/KrNKdtP69XB3c8OFM2fRs6suJ0AnSZlCIJYpWQojhhvi+XPJlY/WYG5mxpGZTG/cxK0bN0GfipMiNr148YLBWrFeys+6cO48lCtZGmoqBWHQv3+mmXYuX7iICqXKoGixoihargQKaWlCvYJm2qe6liYKVizOZ6EKmtCooInCqZ+K31T2NSfVV03tS12r+Ad9Ufui5UugZOmSKKymjt49eoJU6f+VQ4BpJmCam8xZjJ0z4JxldGYDJCGZZhfTI1UsBWjo16s3goOCPjsMBbmnbDFk4UuJwkl1mtkRER6OVctXoHKFiqxypWwxBJjuqWpRsgK+fOkS77NqlS3HIF2sUGE0qF0Ho0eMxPGjR2FjbY2oyMhPhiDVKYG1Wv4CrH6Ni43D4kWL2C+WwhOSha48Ph5HDx5C6+YtUKJoUWgWLYaO7TuwVB2Zrk8bGxu0bdMG5UqV5lR0pCZOf1J6Olrro4ePPpgHZdBZt3oNGy/RfvLKFSs46MUHlcj//MIVVCqvhXxViqPYmJZQXdYZKkYdobq4Ewou7sin6jJtqK/oAlWjzlBdLF2j6992prZbpg215V2gtpT6kvpQWdwRBZZ0QIV5XVG+Q10U1tDAgD56SE+Hj+ed134LMM1lMCUJmNSXWcbYc3k9P+U6hAFStvK1ebPnMAgN0NfnYCVfGozCDRK4UKCGBvXq4/JnJNQEeQLOnzmLti1aonghDVa7dk/do4xLdZ+hQBHXr15la+D6tWpzzlMCSQpV2KRBA4wYNpxdVu7cvg2SCOmgfdiu2l1Ae6aHDx4EpXhr0bw5q14XzpvPsX7JGIqkUfJ/rVWtOowWLYazs/Mny3v06BEqaVVElYqVMFBPH8MHD2GAJyl68ICB3Jb6uXjh05RxMdHRmDJhImj/tV7tOnjz5s0n/d9kMC2PX2oWhsZqHfx5wQC/n9HDH6f18Nfp/ih8fBCKGfeA6tKOKHhYD3+d7Y8/Tuvjr1P98Rd9cj2qm/n5Z+o19VMDUWJTXxRc1BGqe/sh3xkDHuf3M/3w6/l+0Do8FOX7NYVGoUIY3Ke/ANPvYahkkJM5sHwuwPnnrikn4SjmRIYw376mL8+LjHncXR3ZqCYmKm+qML993crR/D85ngDTTxh0VhWkJKdg6qTJUMmfny1gv1ZSIYm0Qb16UMmXH3Vq1sKJ48fTjJIymhulYCPXGa0yZTkOMFkHDxk4EBQZiVS0dJDFrZWVFbZs3AT9Pn1Rs2o1VuOSGriomjq7yZCV7ZyZM9mvlaIpUaLxZ0+e4ND+A2zgRDGFe3TTRYO6dXkcUh8P7N+fM9uQNJvR8fTpU2hVqMBhOD09PDgKVFRUFM8rLCwMQ4cOhWbx4rh0SYrk9nEfZm/f8ngaqmpYuXLlJ36wNy+YoFK5cvilViHkW90Bv1zohf+d0cX/zurit7N9UHhbHxRoWxG/11LHr0bN8b/zffC/M93xv9M9Uk/6/vnzF+6vJ9QO9oV6j9r4tbIafptUD7+f6Yv/ndOVxjvfDcUP90fJvo1QuJA6hvbun6HU//H68srvHJFMaa/r/+y9BVhVa/ct/v3u/d97P+PY3e2xC0EECRNEwgZssQNFsbsbsbtbEUEBuxMbEbFFRBFQMUAExPF/xtwu3CIpocez1/Oss/de66314nnHmvOdcwxVtKkq4pQLphIVSgDlfUZ3PnrgL4QCvMb7jO5U6Ya+ktQXXlPVVamXMBiFcl5Mi2FZWnhKewkXZdblPeU+++R3VVDLt6hfjpNlOR7eZ2qKv59PfL2E7SrP8n1bjExWqeXEj+uj6jlZnn0zoImkB8yxtR/UH5EfVCkvSvscA8uQrk81Dyqw5bNGR0XIuJX5UOpoPn8RkMuLoCbPNDMWNeZj2nXvgZzZc8B+sH2q8jOVcRw66AVdbW3kzpFTgG7BvPl4/4680YkfkRGR2LNzF6zMWsleKiXY6N4lScPGDRu+i4plysmF8+ex2NlZVGy069SVfVVarLQCabUWyp9f3LZXvC9LGwxmolYqPxkYRJm3lStWfkdEkdjISMLPKGMrCwu8UWOAYtm4uDjY9eyJIoUKi0s6sfoM4qFgOi11KwvLHwDq6H4vlC9TFv+pkR85Z7fA/3Zrj//Z0xr/cbFCNpeOKDu3LYpWL4M8JfLj/3NogP+71wb/49Ia/2t3G/yv3W2TOHmPZVTnf/a0xv92aY9iq6xRSq868hTOi//TqSpy7OyA/7i2xv/sscJ/XNug+ObOKNlOG/ny5kH3tjaITkU+bmLP/CuuZQmYkmjgUyTTN0jg/lLAgQBBJRcSBtDyi42OwqyZ00SRJfzN96LUBCYVUBBQmNf4TtIjmKvI/EoCIK8/C3gcT+2nDizsi32EBAfhedBTkXVjefZNyzD8TYiApZLuQgALfxWC6E8fQUL84Q5D8PpVqICgerv8TmCkbBpzY0lk8C6c6TYfRFib/bIM8yVfhT4HaRFVIBkhEcJvXodizChHdO/aGR8jVGD6bQzv8SzwMUJePpfUEfbB9mI+fcSLoEA8e/pY+uFYGQCTcFya31k4J1+9KhrShoxfwhiAQjCj5Brp/JKy3pLq+czp00IxSim1EoWLwH7QIDDFJLmDZAurl6+AadNmIkrO/M3CBQuhkb4+hjsMw66dOxEQEBDfBMkV/P3uYOf27XAc6iD7rlUqVBSChV497bBu1WpUr/y3sCUxcvfvihXhOGw4bt/2jW8juS/nzp4VYgarVuY/7BkzT7Zn9+4C5NzfTerg3myRAgUlL5WMSQzkehrwVD63rtsoYPr/qhRBseEtUGBJO+RbaIXczpYo5twOtUe0RoUqFVC4RFEU6K6H4os6Io+zFfI5WSG/U5skztbI7/TtzOVsiYKL2qLalA6ool0TRYsWQn7LGiixoC1yL2qNvAutkGdRa1SZY41KLbVRMH9+tDezxN07d/A8KEj2mLnPrJyBAU/jvwc8CZBobkYz/8oj08E0Nvojbl6/hkkTxmPs6FEYO2qksAGRArBj+9bo0sla+F5PnzqB2jVroGKFcqL0Qtk0MgjduHYZX+JiRVVk04b1QtFH+bXxY0djuMNgHNi/V6w7yqvZdOwgrEBnz5wQ8CSgKIC2asUyWJq3QpfONkIEcObMcdj17ILWVq2EWpCqJoFPH4EUdhwreWF379wB3QbaKFe2tLAAKZy2ClAp1u1+Nxd079pJ2JsG9OuLgMcPcc//ttD0TZ0yHh3btxH+Ycq1kalpz67twixEaTSDRnro16e3vGhwrDwJjs5O80TblRJkZCA6dvQgKDZOMnsyA1m0MsPsmTPwOiz0t8u7VObnX/OpAdNMW8OYZkLyA5I2zJ09+6f6oQu3i42t7HUyf7RF8+bw9PRMNBhHvYPgoOfYuG6duHTprs3zlyrvkwE/jY2M40kdyGik7n6mCMWZU6cxd9ZsdLK2kYClIvkLCJiSeILRtzFq7EvqfSb2XQFTRreS9EH9oGXaq2dPSa1JDkzPnT6NapUqo3TR4pLDatzIQHRa+aldu46QQxQsUwJ5a5VFDu0yyKFVGtnrl8Jf9UujcK1yKF2mFEqVLol81Usjp1ZZuZdDq5SUY9nET95Xndnql0L2+qWRv045lC5fBqVLl0TBKiWQS6uMXOe9bPVLo0DdcihRqTRKFy+OKuUrCDEFiSoYtJXUaaDbEE2NG8PJySneJa8+R1n1PdPBlBJsRw4dRNnSJeE4bCiYt7lo4QJYtGqJ06dOYtbM6QIs5Nolab25WUshOr904Rx0deqL1Bgnw37QACGhv+17C3Vq1UDXLjbw2L8Pbvv2oGaNapg7eyZOnjgOAwN9kJpPIXWIi/0E1z27oK1VD+S1PXrECw/u3RZS+WPHjuDUqRNo3qwJZs6cBr87viIV179vH7i77YPPjWto18YKJibNcdDrAEJeBMaDswIUtDSvX/PGyRPHcPTIIRjo62Ht6pXwvngBlSpVAJVlTnFcjVSSc/7+fqLywheC8+dOi1QbZd+ixHKnqHcMKHWmXV8L27ZugqfHPtSuVR0rli8RiTad+nVFFo3/4+jq6GDNyhVCbKByh2ehNaYJdPrmDdCAaaatV4xk1dXWQcH8BcTV+rMd0RKbNH4CKpYpKxG2tA7Hjx+faMBPwj4IjrTmSNWnVau25ImSm5fMRgRS5qWaNmuOqZMmgfubH7+6Jp89DYSedgMJMGqgVR8LnZzw4sWLhM2n+JtgWqFsOZi1bIkHDx4KcJMuMDz8LUJDQiSdh+xNyYGp700f6NSpJ9Y5o5aZelLg61k4f36UKFwUJQsXQ4mCRVCiQOH4s3j+wihZsAjKlywtTEulixQHr8l13kvDKfUKFEHZEiWlrbLFSqJk/kIoIaeqLfleuLAEghUvVFhegBT6xKQ+C36lVyxdoiR2bN+R4nxmVoFMB1OqkFBrU0+3AR6I0gjQt7cdDPX1sMh5AQYP6AuTZo1FMHz0yBFiEfJhad4b6Onh2NFD8uzDhg6GwxB7+N7ygZGBPo4fU10/c+qYEN/TEuTBMr3suqv2G6Pei9C3w1B7IVrnHiVPAm3Ak/tYuWqZqLY0adoY4yeMxc2b10X6i0CoHKNHOmLIkIFSj/uiBE+Va1cFXLxG8fNlSxaDgub6DRvA2Wm+cMkaGxqI65dtDR44AOPHjcX2rZvR2MhA3Le8PnXyBFCOjHum3KclmI4dPRId2rUV/l+S+3dsbwXKxs2eNQNtrSzAFwQepE6jTisZozQRwb/wRUIDpvLvMTP+Qzdt1cqVUapECXjsTzrNJTV9kzSBLtomBoaSz5kvTx40NjbGmtWrU8W2Q1curSBy8fbo2k32b0+dOCn7sXQFc6+UY7W1scHmzZvh6rIXdavXFFfx6lWrUjPERMvQyq1QpqzorZqamsreKYOYyJxkaWaGSuUroFiRwtjr4pJofV686++PhvW1Zb+WY1+9chWWLVmqOpcuxYolS7Fq8TKsXrQca5Vz8XJsXL4Gc6bMQK2q1VGycFH0624n19YtXvGtnFI+mc91i5Zjw9LVWDpvIYwa6suecvtWlmA765aswNrFy8Eyqxcvl7EsW7oUcipjTOKTGtjOCxfCoGFDYa0aM3JUsoFmSU5QBtzIdDAlOBz08kCjhrrw86WmZxx62/UQdyy1Pg8f9MDVyxcRHRWJgf37yv6k/PHv3BYAPn36pDym4zAHDB1ij1s+PjA2MsCRQ55y/dTJowKmvjevCdH4kMEDQfcpg3cIMqTicxg2BJ06WYN6qDy4p2nXsztsbDrAxWUPWpq2wMTxY+Fz8zp0dRuIxchxfo6OEmL4wfYDpG1VsJJKW5WWKcGPXLsklu/bpzdc9uyGoYG+CJefP38WRgYGuOvHfZHP6Ne3DyaMH4cd27eikZ4egp+rSLAnTRiLnt27ipSZgOmXWEydNBEtmjWVa+yztaWZCGtTgo2i47ExKpJs7v+oXhwYWMX0mozV+lR/aVAs8dR8/my91LT9W5YRMNUEIMn/XBn8H/LLFi9cRNI/LqqRGKSnm3t374KybhXLlUOObNnERcr9yK2bt8j+XGJtc692yqRJsg9Kq5Ygz8Aeio/nz5MXdarXgJ6OjhAyZP/vf1GscBHUrlEDFUqVEUuM+6k/e9zx80OLJk0FUBkoRO5d8vHGn2XKomGDBjh7JnGRdPZ7/+5dNBQLP7+8PKRlLBQPaKijA5JbbNq0KS1VfyjLNCOb9h0kKGzc2LHOVETyAAAgAElEQVQ/3P+ZC3SZ23TsKG0yjSqt++o/02didbIETD0PuKN+vTriNuUg1q1ZBSNDA2zbugWHDx/E0aMHERMdKXullEpz27cXN69fhp6eLhwchmL/fjcYGDSS7zdv3pB9TC9Pd3me48ePCAD63iCYxmFA/z5irQmYRr4DPn+Cm9teaNWrIy7lvS574O19Ea1amQkA0gVtbNhIrMFbN6+Le/XMqeMil8a6kyaNg66uNvbs3oE7t29iodNcIZJnQBN5gIMCH8u+56hRI+DhcQBaWnUxb95sUHhcX68h7tz2ETDt1asnRo4aAX//OzA0NMAIx+Fw3btHdFW536rsl8bFRsP74nnoNdDGwH69ZT+42t+VQKFtquro6GgJWG/auEFk06gIc/XKRYx0HIYH9/zlJeK3BJw/2S2sSY1JbG3JkGvOCxaIxce9vQeJ5GD+bCe0Url3SWYgKrIw15NE9a1MTMUdy3QSdWWX8+fPo1b16mB6yZjRKmlHd3d32Wsk2C9csAD+d+5g7erVsLW2liAjgg/3V3maNGuGSxe+Fx1Py9gJqARwupHJpMR0G+XkdYqgJ0e99+jBAxWY5suHNWvWpKVrUeIRV3u+/Ni4YWOa6iYszCho63btkStHDozNIDAlY5Vth45C/0jL9I8FU4IalUfmzZkJam3SUgx/HYolixeik00H9OjWCatXLsWnqPfw9bmGgf37YNSIYaLwsm3LBrRra4nx40Zj7pyZ2O/misCAR1gwf460STm3O34+WDBvNp5K2x+xc/tmbNm8QazGSKbMRKgiajdtXIvOth3Qr48dLl44K3Jtdj27YuzoEQJYngfchI93/tyZIp5Ni5AnxzR4YD8pt2cX9VIbggFOJJSnBRb18R1c9uxAj+6dRTJt+rTJOHH8iMifMciKYEsCeT4LAflz7CfQIu/erTMchw3BzOlTZMwcK9uLfB8uiinel85hzqzpWLViCfT1GsgnXxYYvNStqy062bTHyuVLxMo+d/YkqL368P4dxGo4hL/tZWYRgGvoBBMumRnzm2BmP3AQSNpOcgL1IJ+M6QGi2LJj6zY0NTSS/UQCYMF8+VG/Th30trPDxvUbcOXyZfTt1VuAVF9XFwxoYiBQ+7ZtJY/VpHnz7+TWmPtJwCO7UoN6WrL/VzBvPiyYPx+fP8cK7R+jcFN7EvhTcxBECKgJT8a4EozJDMXno2uUR0xM7A9lE9aNi/uCh/cfQLe+tszL2rVrxY3KveKEZVP6zWApytd1bNtO6CFHjxolbfxMW0pffGamO9m0a4c82XNglOOIPxdMaSVxn5F7iyr3n0qphZbd6zDKkQXFg4iyJ0mZL1Wd9yKjxt8szxQYtkF3qKotVWqK+m/FwlOsMxFujnwnqiZMowmnXNtXtRJKkjEt5nOMSo4tUly339y4bIOuYqa0fIqKxF6X3Rg0oI9IkbEf3uc4CLqUZXsf/krGybGzj2/jUqW1UB2F1xnhzBQY5blYThkv2w1+8RSnTx7DxQvnJZiJYCoaqnExMlZKlXHuVHOiGgfzT5U5UdrSfGbNPiqJ7qM0RPepWe/TVIbqKOamLcV9Nz6DrJikBjBv1mxJLyHZfDWSMeTJG0+qULNaNXGxlixaTKj/GMjUp6ed5KCSR3fTxsRdnyeOH5doVAbSkAyCdIh0QzK9ZlgqT5Z1VDvl97DhIu02ZhSzI0aBn6O+fvJ7wnPCuHEY0K+/kEyUKlYMrS0twWusM2rkKIxO5uS8D+4/QFJ7+PxtW7cGxdUT9pGa30xtGjFsGBiMxTQlElyQzJ7n6JEj03iqxj2WbQ4fDp269ZAvVy6MGzP2z90zVQCHAKO+uPM3QYSn+j11MFSAk9dYRgEL9e9sU70+yyjl1PvjNbZDogelDPtOrG31evzOMszvfPTgnpBK8Ld6GbaXUltKn6zHyFu2kbBv3uMLBa1Z7qXaWncQMewN69Yg8oOKTOLbc3z/QqHKtc0a8FB/ds33N9CAaVIQlb7rlCMjfV/RAgWxbs3a9DWWQm2KgzO3kXuPri4uojXavk0b1KpaTdzAjEClG5gpLoweLV6wsHxv17o1gl+opMJoeZEA3/eWL+bOnQttLS0Uyl9A8jsZQcvoWRJBkDxBzhw5xN1Jl2eiJ8vlyCE8w+QaZl0GOYnKTIGCEkSltEeyiMROVV/ZJWiKeaYkk8ifO7e8KCjjUOqp2uL41M5sqrp0VXOPlvVZnmWUesqn0h4/lWvqnxKk9ddfMo9si+PhNfUyqfnOvhkEViR/fmmDLnoKBFSpWEm22lL4U2fa7UzfM/1TFltakgRMWqGZ/UwETIqbM0r4+bMA2QdNCOCZPQZN+6l/MdGAaeasT/tc9oqLlPmRjGjNzGPbli1CNq9TT0vcmuyLLsmTx46hmZExCIbVq1YV61K7Xj0JMCpZrJjkN44fMwbTJk8Ry6pbly7iEiUhPsn5terWFbpAuqkp+8b9VDk7fv20tkanjtbo3FH1GX+f5b5eU+51se0MS7NWopVKYDc2MEDHDh1En5Wu08TODm3aQc62/GyL9m3boT0/27SV37ymnMp19U+22dbSCjX+riLk+kyv4Z4nz4T9sR9eo16scvK39dexMfDIwtQMf5crL6T9Bnr6sG7f4Yd2ErYrv9t9ez6b9h1B/uS/y1eQ/WjyKtt1746d23d8p5yTmf9eEmtbA6ZZtK+WVnAicKuCnFQC42mtrymfejBM71xpwDSxpSV918hmM23SZLHEGMn6Iuh5+hpMofYBd3exQMm3y/1O5aBMmpG+vuRlzpw+Awyg4Z5jyeIlwNxOaqBSkYVuYZLa582VS/Yl6RoeNGAAvC9dEnUYKsRwDzix89PHKCjnD/fV7tHy5f5tnZo1xULcsH6dWMLcS3739m0SJ+8pZ1Jlkr5OdqdXoWHo1b2HBPgQ2EKCg8HrP/aZ+Djev32L92/fyTy479snub7MCXXZswdUtqHqzvdtJfzN8X27FvUxCqdOnEDNqtUk0nvntu0/8A0rf7+s/NSA6W8Kpuld4DX1NWCalQtJRvfFxbVju/bijhw62F4CVTK6D/X2zpw6Jcos5UqXwUFPr/hblFijMgxdtM7zF8g4SJSwfctWMHK0s60tWpmawqRZc9FbJRvRvDlzhLieli0PBthkxEkwvXHjhoiG01LetSPrCArWr10r7mVa56dPnYqfn7R+GTVyJPLmyg0Dff1Ui54n1ge1ammdV6lUSV4wEiuT1dc0YKoB00x3W//pwK6xTDN+2aI4N9mGGAW7ft36jO8gQYtMLalNi69IEWzfti3+7tu379C9S1exyphL2r1rNwyxt8fwoQ6w7z8Adl27S1qGdbt26GRtjV52PTFksD2GOThg8MCB6N+3Hwbw7Kc6+Vs5eV35ntzngD59pT4tXbqBK5UrBwYDbVMbZ/yAM+kLI3pJosM9XEq/vXkTnuaeyBhVrUoV5MmVC5MnTUpX1O3SRYvkBYc5+xmZMpXmh1KroAFTDZhqwDSd/wY0qTFqK0oGfWW+JoNd6tSoiZs3mauduQcXZEN9fdEbVVJHlB6pDmOo30gYdijpRjk4gkrev/6SYJ4CX928dPdSDYY8wn/lyCHlSND/F4N4cjBgpoAEU5HKTwJvsmWXT35P7sz99T77JrewCIMXL45169YpQ8z0T7rdFzkvFCUcWsWTJ07Eh/cqabrUdH7l8hU0b9pMgo30GuhKkFZq6iVWhhb6aEdH5MyWDe3bt5f0psTKZfU1DZimcyH9060uzfOl7C7WWKYZu2xxP6439+goGdbKHGQs4r4g2XOYc5kZ6iBhoWFoY2EpoDd7+vQfHojMa9OmTkXvXr0k/7RP716q7/ydwtmvb1+0sbQS+TVGxbZs1hwD+/aT3NW+dr2QmrOPXS/079sXna1tQEUaRvQypSUz5uKHh/96ISw0FD27dZN9bAI6dWZp0TNfNqmD+bgUX29iZCwvAuVLl8bmdLIosU16AXL8NxuGDhmS6VsAST1bwusaMNWAqcYyTee/AY1lmnBZSd/vK97e0KpZC8UKFETt6jVg0dJM6OJIouA4fDhmTZsu/K2MwD2wfz+438nAHAIeOWgfP3qMZ8+CEBwcLJJlr1+9AsnquQhT0i3hGfHhA16+eIHunTqLhUgZtbhEhLoJXKklWlAvR4IBf39/NDE0EiAaPmQoIj9E4NOn6DS1x3aCAp/BwsxMQJ/zQgL/rDzo7u1q2wmF8uSVoCztevXhMHQItmzahNMnVX+HSxcvwsvDE85OC2HdoQMqlCkjFunfFSpi+bJlKar1pPQ8jx4+RGPO5V+5hAgjpfJZdV8DpulcSDWWW8qW258+RwRTknpojoyZgcuXvEGx7VJFisreIHMI6Qalu1Rcpjlzyn4Z8xRJBVipXHnUrFIV9evUhX4DXTQ1NkZLE1NYmZujXZvW6NiunfDBMgWFUmxdbDvFfzJlhXuiPTp3ga5WfWmXYMEAqIw+pk6eLMw/DXUaCLj+TPsEdKbicE6YA3v2dNJ8vD/TfmrqkEyDqUC1qlUXQGNeaZH8BeP/DtX+/htU1RF3dvbsKFKwEMxMTODmug9xGaA5etnbW7iaSxYpKukwqRlzVpTRgKkGTDWWaXr/DWi4eTN0rSKQde/cRYCtReMmmDJxIkYOdxTWIeYsmrUwkf1NrTp1hZmHsmp0n3KPtWC+fEL7R3IDITogAP83W6Jnzv9mA08CE/czCQD8zrzKV2GvMvSZ2Bil1IRdKW9eLFuy5Kfb37t7t+p5CxXGyhUrf7qd9FRkdDKFB6ZMmAjLlmai2UoCfjIbUTOVrmh9nQbo0aUr1q9dh2fPVMIe6elTqevu5oYSRYuhWuUq4pVQrv/qzywBU5IQJLROEruWsExyv0lioLTBnEzle3J1/gn3+BykHMzIsSZkjMrItlNqS+mbz/Un/Z2+e24NmGboOhYSEiKAxqCdKRMnAV++yN5gbEwMPrx7h+DnL3D//gPcuH4dZ0+fllSWvXv2YOvmzVizahUWOy/CvDlzRWeU9UmdR2uOlHek8xvtOELAecRwR4wa5iikDLRwy5YsJS7UfXv3pivSNKnJiIyIRLcuXZErWw7Zn30VFpZU0WSv36bGa31tYVEa2K//d4T8yVbMjJtfgNDgl7jqfVlc7oyE3rNzF44dOQpK1kVGUE0pY4+li5dIoFejhnq4f+9exjaejtayBEzJGkSydy5ACpOQIt793aKUjIXAeqTaU9p58+ol3r99BXLvhr96Ke2mtq3EyhGcycOb2L2svMZn5Jlcn6qxJl9GvT4VeQhk6tey4ju5jvks7Jt/P3IK8zMr+s7SPjRgmo4l6Meqt2/7ivZm4Xz5sXZ12hROfmwtwRUCcxzPOPnEF2DRAifRHGVQzbbNmxNUyNif27duE1YnWnFeHh4/1Tj3eGnx0eJuYmiIR48e/VQ7/8RK3DceNdxR3P5kagoPf/PbPEamgimtEZ7Hjx7EqRNHhTA++tMHBD9/Cre9u/Hk0T1ZbBXrJakFUEVA/x7+fj44ffKoqM8MG2qPC+dO48XzQAyxH4BzZ07EA616O0rb5MNVv66Mjdf4neAc+vIr6X4Cy5D3lXbU20j4neWUa0od5Tc/eU05leuqcqrrBMldO7di88Z1Uk7pU70OrVaCkjJWpR2lffXfbI9lly5egENe+4VRSdXfN55j9fLKd6WM8lv5VMYqvz+QE/l7HmSlHj+jIt8j/E0oVq5YipMnjuLaVW8MGzoID+75ZQklozLmLPnUgGmGLminjh9HFVLFFS+erOB1RnVKAgByxZKQfksSpPUZ1VdQYCCaGRsLeT+jYeku/ZmDkm90TVcoWxaHDx3+mSb+kXXIlkR6RaYLcf5+ldxaYpOX6WBKC3TpYicY6Ovi8UOVSc7fTZsY4OmT+/Gi26TO48nFj9aM8p2AwjaAWMyZPR2dbTvi7p3bMDRohP3u+/D0yWPo6tSH2749wJfo78CMViytYraFuGghludCr1yPv4dY7HdzEckzgkC0Gv8uCeZjYz7Gj0kZl7JIK+1Rko3tcrwswzMmOgKxMapnIrCxjFz/pKIIVPrnJ+/RgqNU3KoVy/Dxw1t8ifskz875YNv8pMD5fndXzJ45FR8j3ss1ZSzSX3QkGBDDcXE+aL2zrPu+PYiLVfXPfpQxKnWVT1qxyvOyT7bDfuPHHh0p7XyOVs2t4mFQnlvV9kd8/hyD16/DsHDhAhw9egRHjhyGXkMd+Ny4Ks+k9PdHfGrANLG15aev7dq+HaUKFxVVFldX159uJ7UVr1+9pmIVKlAQWzdlrmXKAKJZU6fJ3ix5e2/evJnaYX5X7sSx47JfTGHyObNngxb2v+F49jRQ5PKoKztv9pzf6pEzFUy5UHLRfhEUgGZNG2PxImeEhYageVNjrFi+SIDhgLuLaHFOGDdaBMG5aNP6PHRwv2h1kuh9r8susWZHjnDAgnlzEfg0AI2NjeDp4Y7AgCcwMtCDx37Xr2BKK5LqMB/gsX8fhtgPgpPTPGzdugl+t33w5XO0yJuNGTkc7NPX5zqePnmEDu3aoHrVv+G0YC4e3PcTACEAhrwMwvr1a+DgYI/Ro0bg0sXziI5Skd3zfljIc1ALlWC8ZNF80WGlsPmEcZQUchCLmYDzPjxMNE2HOQzCyhWLEf4mDP7+tzFl8gQMH+6AI0cOIfpTJM6ePiGW3OuwlzjgvhdbN2/EmFGOmDVjKl4EBcpY27VtjepVK4uuK8nwOceca5Y/c+qYyLS9fxsGN9dduHThLA4f9ACFz1+FvsTqlcvhMGQwxo0dKaLi6q5tSsPdv3cHM2dMxbChg0U/9lNUhIiV79m9E8uWLsKI4Q444OaKfXt3Y/hQeyxf6gxK2/FvvGyJM4baD8TkiePh73cbHz9GwMPDHf53fHHs+FE0NjYUfdi4WJXL/48AUm5NaMA0Qxc16ovSSqxaqTKOHzuWoW0n1tjzZ89k35SUgQvmzUusSIZeu3juvEQfk893kbPzT7VNflyz5i0kRcbG2hofPqSeQOGnOvxNKl29fEWiiIsVKoydO3b+JqNSDSPTwZQLJq1KggKZ/kePcIRNh3Z4/+4N9rvtFWtl6uSJQsNlbmaKZ08fY/zYkejSqSO+xEbj+rUrMDYyxPmzp3DtykUEPn2CgCePYWRk+BVMHwuYEkhofUa8pyUbg+PHjqCBjjZGjx6JyZMnonz5sti9eyduXL8Gi1ZmAsoEhi6dbHHq+DGYm7VEtap/C5Dc9b8lYEqLLODJAyxbvhSrVq1Et25d0Lq1JUJfPpf7BLGAxw/RSK8BTFs0wZRJ4+Cxfy+sO7TBtKmTMHnSeLS2NMPjh3exdMlC6DfUwfixo7Fu9UpcvXIJnTvbYtSoEZg1axZamLTAlcuXRHpt6JDBMg/6DRugk401li1ZjKaNG2Oo/WDcuHYFpi2aoXrVKpg+bQru3vEVMKUVOWnCGLRrY4nYmCj43LiGJsYGOODuis62Nli5fBlCgl9g6ZJFWLN6FWxtOsLGur24tynMzpcPvrh07doZw4Y5wGnBfLRo1hRnz5zGjOnTULdubTg7O6Fnj26oWKEcxo8bg8kTJ6Ba1crSx5NH97Fo4QKsXbMabVpbYUC/vggNDUGXLp2wZvUKnD59Ek0afwXTr/vnGjD9rdaC32Ywm9ZvEBemrrYOfH1uZfq4uAfZrVNn5MmeU7Q7uS+XmQf7692jp+x5WllY4OXP5Ip++YKRw4aD8mO0cH19fTNzyL9N22779kmgWKUKFXFOTZDgdxhgloAp3Zjvwl/BumMHlCxRAl4e7gDi0KWzDewHD5B5eBkchMbGxmAi9qQJ4wRM6Za8dvUy9Brq4tyZk+ISRlwMHj68/xVM3RAY8AhGhg3jLVO6ZT/HfMKYkSNg3aGt9BMWGoymTYywZesmODktgL5eQ7jtcxVArVe7Fu7c9oWz03x069JJ8gU5XmWhJ8jcu+ePgwe9MHPmdOjr6+Le3dv4TDfup0g8vH9XQHLb1o3yHKtWLIVWvdrYsX2LgIiuTl3s2LYRrcxaYPmSb2+he3bvQI0a1bF27Rps2bIJWlp1xHKdPGksBg/si6dPHshLAq1yHm6ue1G/Xh08fvgATvPnolvnToiKjIjff4z7HIUL507BsFFDXL92FatXrkSXTtYICX6GNpYWWOy8UNrxv3Mbhw56CRA2MTaSfvhS8CUuGl5e+1GtWlUsXboEu3bugK6ONlavXIGpkyehk00HqX/8+GHU06oDtkNLuqWpCRY5LwDwGb4+N3Dk8CE4DneQF5aAgCewtu6IFcuX4oyAqYHKMiWYJtjDVub7H/mpsUzl30ZG/Idu0DkzZwlVn7mJaaarxXDM3HcbP2Ys8ub4C21aWYAkD5l97NimCkQqW6oUPPYf+KnuaMELtWCx4tixfftPtfFPq7Rk0WKV7qyOLh4+fPhbDT9LwJQLJMFzySJnGBno4/Wrl7J/aWXRCtOnTZYJ4T6lpYUFVixbJm7Cnt07y/U7fr4wNDTA+bMnv+7bUaT7PoyNjHD0iBcCnz4WMD18iJFxsbLHFxsdBftBA2HXo6u0Qbo3czMTbNm8EVOmTEbDhrpYvHgh5s6ZiRXLluLtm1eYPnUyenTrgrjP0fFuU1p7Z8+cgIWlOUaMdERPux7QbdgAD+7fUYFpdCQe3L+LxkYG8PLkCwIwf95s1K1TGwvnz4bTvNlYvWKJjL2Rvg7cXVXAyHKbNq4X4Jo1ayYWzJ8HpwXzcM//NsaOHoEhgwcg4Ml9NG9qBC+P/dLuhXNnoFO/Hnx9bmLu7Jky1s8xqrEyQpZ7vR/eh6O3XQ9MmTgB/fr0wsb1a0A3bWsLc6xetRIXL5yHpbkZxo4Zia5dbNHU2BCBAQ/lefElBi57dqLK35UxZcokGc/8uXPgd9sXE8ePRY+uqr/H0SMH0UhfT8YaGREO/g3XrFqBY0cOwaJVSynboW0bWFmY49mzp7C1tcGqlcvFMm3a1Aj3/PkGHSv7wdxn/SOiezVgKv9GM+I/lCBzsB8ijDndOndBZGTWkGGsWr4cBfPmRUNtHdzz98+IR0m2jcCnT9HMuLEEIg0eNAjR0Wm3hm/f8oV2nXqiwjJ6VNZSCyb7cJl4kxSKuXLkRJvWrTMlFzg9Q886MP0Si5XLl6JZE2NwP/BLXCzGjB4JM9MWuHvXH9QTbKCtjbOnTmL61Elo2tgQD+7fw5bNm1CrZg1x8ypBME8ePUQDHR1ZpAlmOlp1pG0CBxdo7ovSrUxQ417p9m1bUKNaFezZtVO+GxkaiPuS7mL+jxMb8wnTp05BYyMjXLt6Ba/DgqUdWltzZ8+AtrYW/Pxuw9l5IbS16+P+XT8JCKJ1+vCeP/Qb6mLfV6B03bMLDRtow9Njv+zt3r93F7S6uSdLsPa7fQuPHj0QkNRtoI0dO7ZJubv+d/A2PEz2M/v36Y2gp09goNcQ06dOw5PHjzBm1Ei0NGkhwD9vzkw0MTbE1SveMpd85oj34aDV7rJ7F6pUriRlnz55KBY26xFM58yaBcNG+rh7l/ui02Gg31Aio2mZfo6JwhXvi+IaX79uzdcx+SH8zWsMH+YA6w7t5N/ZYS8PaNerizu3faRtM1MTcSGPHTMaZi1N8fDBfYwa4QiT5s3wNOAJ2rdvh2VLF8t816tbGyePH8GjB3exbctGcSvzJeAfaY2qp3FpwDQ9a9B3dcPDw4V3NXeOnHAcNjzLuGcPeXqJ+7By+Qo4fjTz92n50LOnTRdiCrppfX4iECn8TTi6WNtImohlK3O8DA7+bi7/tB/M0+3aqbOwYA0ZPBifohiY+vscWQamDMJx2b0dgwb0Q1jIC8R9jsH9u3dg16MbrMxboWWLFpg1fZq4ThmAZGyoL3urQwYPRJfOtrh185oAGIN+6DLmviLdsvf872Co/SB062IrVhZdtFyg3715Lft+trbWcBw2VMB057YtePf2DZhWw/1ZukGnTJqA929f4+L5szBp1gydrDvgivf5r/uQUbh04RzaWFmgZ/eu6N+vD/r0tgNBKpZRwlEfEPjkEfr2tsOJ44fkBeFN2EtxU7c0bSGRx6NGDEPwi2e4dP48WltYwMy0OXjt8YO7WLJoAVqaNENnW2s42A8SkGYQz7w5s/Ds6RMY6hvAtLmJPKdZSxN4edJK/SJBTSbNG8PWuj2ueF+QeSEg0e0cFBiAdm2tMHXKJImaffs6VIKJXPfuxmVvlWVK65WW66CB/SVwiHvDBGTO2/Kli2Fm0hxdbDpiUP8+uOVzQ6x3Bmt9+RILWsh23bri8YN74gXgXLru3YMTx46iVUsTDOjbG3Y9ukugUvDzZxgz2hE7d2zB86BnsOnYXtzL3MflnPr5+oBBT38CmGroBDNmUQtiMFCTJsiXJw/mzZ2bMY2mopVbN32gVbsOihYsiE0bVFs2qaiWriJXvb2FErBg/vxYsIBbJWk76BKfP3uO0PaR2o8MS3/y8TQgAM0bN0HenLmyJFAsrXOZZWDKFAtapC9fPBPXXuT7cAGsV6HBki968/pVWdCZWkHApMvT++JZvAh6KgE/796EyeLNhZcLP61HRrIy9YNt8PvbN6FShn29ex0mFh2tWzdXV+jqaOGQl2pvguB59fIFASUGB7E890YJkr4+1/Aq9IX0wb44HgbXXL50HkGBTyR6l31y/Mx/pZsy+Hmg5HMyB5TlaSXeuHYZZ04dx/17fgIWn6OjJBrX++I53L1zK/5Z2R8jcO/63fr6LC/kZeHe3TvQ02uI9evWylwwrYhgqQK9d/FjVaxoBZC4ZxwSHCSBRZxHju9l8DNxrfNFg1bhlcsXEBz0VF5q+CxKXZYnsPrf9pEx3fG9gXfhYXgVFhyf18o55vMq9V6+CMSbVyEyf8whvXblktznHErfLwJlPjkvrMe55MsUc4zfqv1NlTH8Ez81RPdpXXaSLk92n3p16qJo4Zh/RPIAACAASURBVMLpVhdJupcf7zwLDIRZcxMhoieRflYcdGFTDYbSaq1amv1UINLhQ4dQsVw5FC5QEKtXrc6KYf+yPrwvXUKtGjVQtFBh7Nm1+5eNI6mOswxMuUgqi7X6gsnFO+6zKp9SWfwJbnQ9MqiGJA8sw2sJ67GMCghVeZNKmZioCNz398OwIfayh8jgn4kTxgrYsg8u7LSU6TZWgo2UPrlPqnKbqkgeeJ35m59jVXmVCcci979adtybVZ6T7Xz5HCV1lXGzL/arjJv9sByfk9cIxhwf3dQPHtxF+w5tcPiIl/ztlDoEqKTGqt4Pn5FleY1jZl/KC4z6syh1lE9lTOJSj1HNb/zf7WvffA6O4bu2v/7N+Dy8r3qpUPXN+izL6zz5W3kepd9/8qcGTJNaXtJ+/fSpU6hcoSLKly2Lg16qf/upbYX0fEcOHcb2rVux330/ngYGprYqPrx/j149GWGbHf1798bHjx9TXTc9BXft2oWSxYujVMmS+Jmc2iePn6CJcWPRTh04cOCvpRZMz0Skoq6bm5vMVZVKlXHu7LlU1MjaIlkKplm1YNI6exf+Gle9L8HVZbfkrdLCIqgoAJNVY/mpfsjIFB4Kf39fvHz57Dtw/6n21Pf3NN8F2DN0HjV7phm2arns3oOSxYqjRrVqoDpIao/7/nfRs2tXlCtZCoXy5kPxIkVh0rwFPDxSHyk7bcpUCQhqZWIKWqpZcTAQybR5cxG6/hkwJIPS4IGDJCjHyMgIAQEBWTHsX9LHsqVLJRWokZ4e7t69+0vGkFynfySYcqGk5cTUGkapMv9UsZQydBHNRGDi+Gnl0Yr7R7wAZOJc/PZ/Mw2YJrfGpOkeo/nJyUsS84cPHqSq7sfISBHbZs5lneo10KJpM+H2pYpMzarVcPjgoVS1s3nDBhTJVwDadevBx8cnVXUyotCcGTORP3du1K1VW8j709rm2jVrUKxwYbHmjx45ktbq/4jyTF8aM2Ikcv03O8jJG5YF6UtpnZg/Fkx/+wX43ww+f9iza9y8aV12ki4/ZdIk2be0aNUKoaGhSRdUuyP7rLVqC9HD4oULRRTcz9dXIl0psdajWzcQcNUPsu8lZOA7eugwKpcthwplyuLY0aPqxTP1O4XNa1evjgJ582L+TzAwXbt6FXVq1gIZleaSWvAPPCjszsjlv/5fNgyxH4LYRMTbf/Vja8D0D1vYNS8R3wsaZMV8cK/8o0YcPN1rWXR0NPr36SupHnbde6SaIm/v3r0oXaIE/iYrzplvEa3OC5zELWigp4/HCZRVEgPTO763oaetg0JUq1mTwWo1ycwO92f79OqFv3LkREvTlmkORHr39h06tGkr1IIUO4/IBNmzZIafJbcCnjxRcfL+lQtzszDKOy0PpwFTDZhm/B7iv2xOCaZRGjBNy7qTaNmQ4JewamUuoEBB8NQqgsycNl1As7GhEZg+oRySO1qiJJg7evRwysoqr8PC0NbcArlz5sSECRMQFxenNJXpn3whKFGsGEqXLAkXF5e09fflC2ZOnYYCuXJDW0vrj6QWvH71qvAZlyxcBJSxU46E3gXl+q/41IDpv2zhzwpL7d/WB928mjzT9C9ft31uxeuYrl+zLlUNiiSXbSf8lT07+vXu850Y9f2791SWZt78WLp4cYrtfY6JwYDefZE7Rw7Y9eiRpRbe8+fPYWZiIoFI/fv1S3NU7iEPT1AjtVTx4tix48+jFjzg5i7UidUqVQYjvn/HQwOmGjDVWKbp/TegCUDKkLXtsKcXKpUth4plyuFEKlmIuF9KQnzuFy5bvOS7cXxjCMqBQQMHpko7dMbUqSiQOzdMmjfPckah+XPmCiNSnRo1cfny5e+eJaUfDNYy1m8k0chjR48W8fOU6vyT7vNvWyB3XjRqoIsH9yjd+fsdGjBN70Kqqa8BYw2YZsjKtmLJUhTMkxcGug1xN5X8uK4ue1GqWHFx5SaUayND0Iyp08Rt3LJ5CwQFPktxnBQHL1GoCOrWrg3fW1mrxHLl8hVhYcr3Vy7RKOX4U3tw33Vg336iRGPZqhWCX7xIbdXfvhzd/aNHjBS+5jZWVuBL0u94aMBUA4YaMEzvvwENmKZ7bYv+FI1hg+0l+KirjS3ehqe8YDLHcsLYsWKNmTZvAeZsyqGGQXt270aJIkVRo0oVnE+FZNeJY8ckorc8I3qzOM3k06dPGNC3H3Jlzw4+z/OgoDTN6+oVK1AkX37UqFIVF86fT1Pd37lwZESESOTRlU8PAwPVfsfjl4EpmXZ4/tv215J7XoVVSL1MYtfU76t/Z9m0lFevq/mejihgDZime20jJy/FrvPm/AvTJlFJSg0Rk2j9xfPnMDc1FTAdMdzxW8CSWtWrV66gXq1aKFKgIDZv3JRES98u3/Xzg07deihaoCDWr1377UYWfXN1cUGZEiUlOnnPrm8qU6np/sLZs6hVpapEI69Z/edQC74IChJO3jw/YbGnZt4yqkyWgCkXeKHL+wqeCqUcF3DN4q9axMnOxDlSBzUSNpC4IbUvHeTu/cewPKXXGvyd6mvANN3r0YVz51C1YiWULl4cqQWRM8dPoEqFiihZtBh2JqHnGRIcjNatzMXipWZpShHCYSEhsDBtKWTqkydOSvdzpbWB4OcvYNbCRFyafXv3TlMgUlhoKFqbW8izDuzXP0110zrOrCzPSN66NWrKC9GObb9vcFWmgynBMvx1CEiC/irshQDGiWNH0NuuO+bNnRFPgq4OIv+m7+Tjjfn0AYcO7hd1FT47wZMvHL4+17F65TI8D3ySJIMTyfZVnL7v4eqyCwfc9+JTlIoL9980j7/0WTVgmu61de2KVSicNz9069eH761bqWpv9vTpssfaSLch7iegl1OM07jPn+E4ZChyZ8suuZivUmDOYY5mr+49BJD69emLmJi064ymavDJFHKaO19SferWrJUmSkU2OX7sOHkRMDYw+C5NKJnufvtbB9zcUL5UaVQqVx5nz5z5bceb6WBKy8rP9wbatbHAhfOnRTeT2ph2Pbvj1Imjom5C8FCsMIIv6fM+RZG8noTqbwSA8SX2Gy3gu9fxpOksy/qJWXC8ThJ5Ugqq7qssZIWiT70e6QZJO4gv0WLdcXEmyBHUSE5Pi09ZsFmPViRPqRMXHU/7x7a/SDsxUlepI2W/xAi5/sdIlSi29IlY+ccxccI4WHdsh4gPKqUajtnN1QV6ujqiPCOF4qLlWWV+ON4vHG8M4mI+IvbTR9j17Ab7wf0R9/mjjI3PTMJ6llEfvzImzWc6XLvqlrEGTNO1wFGXclDf/sibPSc6W9sI6XxKDYaFhMLSrBXy5swJ+4EU1/5+H00BU7azculyoSjUqVcP/n53km2aluuk8eMlaMnS3CLVLEzJNprGmzevXYdWrdrIlys35sycBaQhEGmvi4sEZHHPl4oyf8JBikkSaYhw+717v+0jZTqYEohuXPNG/Xq1cPSwJyi1plW3tuiIUmaNUl7q8mcKUAQ9e4zbt64LCNz198WWTRvgsd8N4a9fCkg9vHdHZNcIXmEhz0U6TV2OjNc/RoQLyf2mDWvgc+OK6JbevHFFJMoILpQT87lxFW/CgqUN1707sWvHFpFa4/1HD/xBibZLF87gnr+vACaBlPJi/O3nexO7d27FQa/9CH8TKgD27k0oDnm6Y+vm9bh/97aMlUDqd/smNm9aJ3PAZybQvXn1EtT29PTcj379+oi+Kl8eBGS/xGC/myuMDPTh5emOfa67cfzoIZkvtkdZNHd3F+zYvgnPnj4GvnxGr1494TB0sLxAUOLszu2bOLB/L/bu2QFKpWkANYPAUx1I+V0Dpula4J48fAQjvUaSFjJ7xsxUtXXQwxOVy5VH6WLFsWdn8nuLx44cxd/lK4j4t5eHR4rtr1u9WsjyG2hr4/bt2ymWz+gCDESyHzBQRLCbN22KwKepJ91n2gjHnT9vXsyambq5zOjxZ3R748aogszaWFoh5GVIRjefYe1lCZjevH4ZBvoN4OXpJoLYRQoVgJ6uNhbMmyW6pEAcTh4/Cgpq37h2RR5u6uSJGD9uNM6fPYVWLVugk00HmLZoihGODnj/LhyTJ47DqBHD5a3t0oXzsGjVEjeuXxYgIeARkFYsW4T2bVuLIHn7tlY46HEAfXr1xPJlqny0Y0cOwrpDW1y5dAGjRg5Hj25d0L1rZwwe2A+BAU8wbuxomDZvApsObeCyewdiY1SybdeueKNF82YibN6vT09o1auNdWtXICY6EtOmjEdnm/bo18cOnWza4+GDO/C+dBYd21tJu60tzbFi2RLZKx41wgFNGhtiiP0A1K5dCz17dJUXAAVMD3l5oErliujRrRN623VDndo1QZFvkgSMdHQQ0fCePbuhZ49uePH8mYh9Ozo6iCU6wnEoWjQzhsOQgWhibIi+vXuIjqgGUDMBUDVgmq4FycN9v6i9VCxbLlWk9GQmmjB6DPLl+AuNDQx+oApMOJgnjx7BUE9fclEXLnBKePuH354HDqB08RL4u1IlnPpFBAFurvtQoVQZkPFn546dP4wxqQuREZHoTTd19hyw7dgRER8+JFX0H3GdnMrdOncBOZYHDxgIvmj8rkeWgamebn2cPH4Iz4MCZHFfs2o5Ir8GJEVHRSD05QuYtzLDksXOCH/9GuZmLbF18yYMHNBPQC7iwzucP3caDXS0cdDLEyOGD0Ovnt1lXk+fPAltrbq47H0eX+I+iYVIq7eRfkMsmD8Pt3xuolVLU0yaMB7z585Gx47t8eHDe0wcPw5D7Qdh147tMNBviCNHDuHAfnfo1NeClwCvHTrbWuPJw3sIfxUiot906547exJ169SCx/59+PgxEr3susF+UH9Q+LuhTn1s37IJ58+fhaFBQ6xasRQOQwajs21H+Pv5gu5cczNT7Nu7Bw11tXHi2CFEfYyE/eCB6NzJWtzaAqZx0dI+n+vsmROIiAiHlYUZJk2cAE/PA9DV1sKB/W44duwI6tWrA1dXFzg4DIECpl06dUT/vr1kfOfOnETd2jXh5rpbXMMa924GA6oGTNO1vk2bPBl5/8qFZo0bp2qf78mjx2huaIw82XNi7KhR8WQMSeVlElC62XaSoB4G5sQkcAknHPzli96oVbWayLjt3v1rRKhDQkJgZWYue712PXp+x+yUcLwJf69YvET2krXq1MXNGzcS3v5H/T57+gy0aqpc3s5OKb8I/cqHy3Qw5Z4dLVOC6akTh/Hu7SuYtmiGndu3gBapEs0LfMbSxQvF2tu9cwc6tm+Pu3f8YG7eCoudnWWOIt6/lbqrVq7A2NGjxOLkDVqmjfR0cfXyRQHTuJgoXDx/FtWqVRHX58TxY9HJ1hp7du2Ez43rMGtpAvd9rujSyQaenh5YuXIFqlerhjFjRmGYwxAB6Svel9C3lx2mT1FF9BHg6IImmJ4/dwqNjfRx87pKb3H0iOFwdBgKd7d9qFa1KhyG2mP0SEf07NYV7vv2oa2VFVo0bSqu7d52PTBj2lQsW7oYJibNJTiLzzBtyiR0tun4HZgecHNFE2MDPHpI7b4vsOvRFdOnTcW6tWtQ7e/KGOk4HCMch6Fr1864ePE87O0HYeTI4eBc9urZFTOnqsZOd3azxkYyv+yL+8AaQM1AQNWAqfz/+TP/YQRqO6vW4tJ0sB+CT1EpWx57tu9E6cJFhS3Jy9MzxW5pyc6YMlX2QZm/+eJ58oQGjx4+RFMjY+T7KzecnRam2H5mFVi4YIGAYq3qNeB96VKquzl35oxERhctXBhbtnKd/b0P/n1iYmMR+fEjwsLC8PjxY9y6dUuiuqktyxeterVqg6QWv/OR6WDKPVOCqU79OmKFvXkdgibGRti8cd13VhL3EP39fNDGyhzGBo2wYN5cRH/6hE62NujXpzdiY2Jwy+cGGmjXh7u7Gxwdh4mb80vcZ3gdOIB6dTnZFwRMP0dH4vatG9DR0cKiRc54GfwcAU8e4dWrUERGfsAQ+0HSR7cunRAe/gZbtmyGrq4Ozp09I+5SukzDX79CJxtrjB8zShU09NWKFsv0zEnoN9TB5UtUqPgChyGDMGTwQJw+eQI62vXh5uaK4BdBePYsEG9ehaGLrS26d+uGpwGPEfj0Cd68fgWPA+6yd0xr+3NsDOwHD4KtdQd5uVDcvO6uLmjUsAH879wS1y2tzcmTJmCvyx400NYCGV9evAhCYGAAPkS8BV2+gwcPwOfYT+jZo7MEI8XGROOK9wVo168LdzcXvH4VgieP76leYj5kIKAk3Ef8N/3WgOlPr3FnT50WAvPihQphy+aUF/6PEZHo36s38mTLLmkgtOBSc+zZsRPFChVGzWrVcfHixWSrvH79GtYdOiBntuwYPXJUiuk0yTaWjps3r6sCkQrkzoMZ06enuiWSPTC9J2e2bBjh6PjL5MoYCf3hwwcJ4goMDMT9e/fANJcTx46DzFXr1qzF3DlzMGbUKPTr2xc21tZoaWoKAz09Ac+yJUpKDnGZkqWwbOkyxMWph5WlejqyrGCWgOmtm1fFkjtz6hgIppbmpti5fbOAKQNuaCV9jHgnYt4jRwxDsaJF4H1RxeDh5eEOY0N99O3bCxbmZujTuxeobbd+7RpUr1oFwx2Gonv3rjBopCdBRrSEP0a+E7CYNnUimjYxwtjRIzFqxDCcOK4Szt3vvg/FixSC0/w5MtFPHj+ErU0H2HRsj/FjR2P2zOl4+OAu+vfrjWlTJsbvw3KcBNOL58+gRbPGuHrlgoDp6FHDMdR+IN6/fYOhQwbDwrwVxo8dg/HjxuDOHV8cPXIITZs0FlfuyJHDsHnTeoS8fI7+fe3QuLEBRo0cJoFGdGlHfAhXBSDFRcPzwD6YNGuC+/5+QFys7PdyLznk5QuJhm7Xxkr2dSdPGo+AgIeYM3smdLTr4cK5Uxg0oA/q1q6OEcOHwqR5M7Hiw9+EYfq0yZg4YbS4wrm3rLFQM+CFQgOmP71gLVrghIJ586FB/fpijaTU0I0rV1G/Zm0UypMXC+bOS6l4/P2rly+LUHiJokW/Ux2JL6D2JSY6BvaDBiNHtmzo3qVrqtiY1Kpn2FdGOQ8dNEisdgqeBwZ+ZXhKoYe4z3EYO3IUcmXLIUxKz56lTKOYQpPf3477gqhIBkGGIYBWpI8Pzp89h0NeXti1YwdWLl+BWTNmYKSjI/rY9RIxbwZSNdRpgFrVqqNi2bIoUbQYCucvgPy58yBPzr9EXCBPjpxihfLfQ9GChVChdFmYNGuOLZs2p8pj8f0gs/5XpoOpEv168fxpBD9/Kq5S74tnEfDkvkTHKot5VOR7efoZM6ar9jTfq9JfGDDD8ksWOUlE74ugQHyOicbrsBDQHbxq1XKcPXsSly9fQGhIUDzBAa27t+GhcHfbg4VOcyW69vHDe4iOipS6p08dw7OnjySSmGQHTx7dx4Z1q+HsNB8E8NCwYNy4cQV+fjfj21RA/2VwIPg8oS+fCfDdvnUNPjcui/s09GUQdmzfAucF87Bn9w4EBwchJvYjzp8/g8WLnLB8+WJcvnwRMdEfJcJ269YN2LFjswAg80rZB13fURFvJUqXwM1n5fwwOMvP1wex0R8RFPgImzeulfG673PBm1ch4NxwH5eRxr17dZOgJeaebt+6SaKV3799DQZA0Z0OxEgajjL/ms90gKoGTH9q5Qp//Qa2HTpKsEyvnnY/CHgn1uiCOXNRJG8+1KleA9eupN7tR4alVqamwrA0ffKUxJr+7hpFtsnGZNK0aar2cb+rnIE/PPargrNITEGgSu2xc+s2lCxSFH+Xr4izp0+nWI2uVqYXMc+WljktSUYynzt7Dgf2HwA5ixcvdMa0iZMxfLA9enbpgrYWFmhmaCyMUdUq/y1BZMULFpao7Dw5cshLwF//zSagTldtoXz5UKJwERFfr1mlKhpo1Ucz48Zo37qN5PaOcBiGWdNnYPXyFRKhferkKQSlkVIxxQfNxAKZDqZKHijdvYolxO/KHiQXcQYg0S3qvNAJjQz0sXvPTnFrKsDC8rQIEafK3ZT0GYJvXLS4YGmNKu3zngoYXsdbeJJrGcf8UdW+p5IL+vHrPijLKzmjqrY+yVgZnRv9KUJeAFRtvpHvfA71/rgHyfrsm+DPcbKdL5+j461klo+Ta6oAKbYX/emDWOcMmuKzsA31fjjOuFjVWFRj/AgCf8TXPFvmmar6UZWRvhGHmOgIdO9qi6lTJqj+6TC/Ne6TvDzMmTVdwFa9L6VPzedPAqoGTH9qibp4/jyq/10FxQsVxrq1KUuuvXwRDMuWrWTvs3/vPqkCX2Vgkm4ykFZeNnTv3AUf3qle3pX7CT9JPVisYCFo166L21lMeK8+Fu4pt7G0lGjWXt17pjo619fHBzr1tMTqd5q/QLx5BKZ7d+/hxo0bOHPqNPa7uWHjhg1ghPOEceMxaMAAdLa1hYWZGYz0G6F+nbqoWqkS6G6li5wiBHzBYKRwrmzZJaCLIFkwX36xNCuVr4C6tWqhkZ4ezExMYdvRWriGx40egzmzZmPVipXYvWOnaMvSU8CxUHrubfhbRH38KAQZSQWRqc/J7/o908E0NQv0p48ReP4sEAud5mPjhnV4/+71d1ar0oYSrKT+O+E15Z76J8skVo6izomVS6ysermUvtN1Hd9GPLirrsVf/9o3f0e+D/9uHCm1r9yXuh++ryvtf3gj+9O06D9FfZCgJtZhfuu78DBJv1Ha0Hx+/2/gZ+ZDo2f6c8vbvDlzJF2Fyfh3bvul2Ii7iyvKlywluaWue/emWD5hgUXOzuJWNNLXl/27hPfVf9NlWaF0GVQqUw6njp9Qv5Xl3znuQvnzo3rlKqkmsKeF2dXWFoXy5oWeTgN0trGBuWlLUeShxBuBj2o7dLXmzZXrq6s1p+xRCkDmzYtihQoJkFapWBFatWvDqJEBLM3M0LUT4zEGY/LEiWIAbdq4Efvd3XHy5AlcuXIFd/3vikX59u1bUMDg33L8FmDKxSjywzt8jlblcdLy+2Zhpn+x+5kF8p9ehwFdtJjV55FAS2tXADfBi8Q//Xl/5fj57zcqKvLfsmZkyHO+Eh5Zc7F0BvcfkGK6Ci2XwX37Ifd/swt37c+4/zz2HxAB7YrlyuPE8ePJPgf3AbVq1xE+2K2bNydbNrNv+vjcEiuTIDdl8mSk1nrjy0qJQoXEwqY1yb1JWpclChWWFwV6BWi9NjY0kmCunl27Yaj9EOlj6eIl2L6VhDReuHTxojBHUZUn5OVLECSjoqJSPY7Mnp/fpf3fAkyVhVBlaWnAU5kPzec/5N/Cuzf4pAHTNK1pp0+cRLUKlVCqSNEUGYzY8BXvy0J2TjAgSPzMQXctrWAKiaekqhL8IhimTZupKP1Sycr0M2NKTR1GxToMGSJg2MS4saSOpKae0/z5KJq/IGr8XQVDBg0C94FXLl+Ondu246Cnp1i5d/z8EPQsSNzATEtKSQggNf3+W8v8VmD6q8BDBeLfu0szbywql7NiHXLvWNlLVv+eEf1n7XP9Q4AvMyxyzZ5pmtdPpzlzUTBXHhjp6YMUeMkdkic6dZqQv2vXqYvrV68lVzzJe2/fvEHH1m1lv4+RpsmR2Ed+iEAXaxvZnyWQMTXvVx4HPb1QoWw5lCxeHDtTEYhEogq6Y3PnyCkMQh/eJ79H/Cuf7U/pWwOmJLSPjkiWyCDyA+kJSXSfXsAliX84PkVFCDcvwe7tmzDZyyR4hr8Jwfu3YT+1f5oY+DLqNymCBvZN8E6snuZaGl8MNJZpmtbD12GvYNuuvbD7DBk8+AepMLox1V2Zj+4/QJNGhhLw4jDY/ueB7csXjBnuiLzZc6Bt69ZITkFG1GYchok1aNOxo+RLpukhM7gwiST0GjZEnty54LwwZSKJG9evS64mLfllS1T0qRk8JE1zCWYgy8CUizfzPxUrjAs2rymfvK78Vl/Mpd5XIXH1+yyvvv8n5SJ/FBxnOfU+lbbV62/bsgGuLjukPaW8qg7Hx/3bcElPiXj3LZBJGZf6mJS21a+pvn97TkbRHj96EOvXrgDTaObMngHPA+5Cwj91ykR4euz7Lqo3fn4SzB2vK2NV75f5uqpI6VfYsG4V3Pftkd/yvGpzwWAkCgNEvH/9w7wnHL/ym59UvFF+K2NT+k/st/q9P/W7Zs80waqSwk/vi5dQp1p12ctjNGlKx6plyyWlopJw9x5MqXiy95l2USR/Aeho1Yf/neQVZBbOmy8u4cZGxmBqza88Lpw7jyqVKqNYkSLYunVrikPZuH695GrWrlY9TSlEKTasKZDkDGQ6mDIAhgs5A2KYuhEbo5Iu48JKq4kpIWQsIsiwTMKFOvZrIA3LMr2E99mOtPdVIo2gmvA3+2R5Wp28x5N9Uvvzy+co+c37lHpzdpqLtauXyXgo2Sbj/DoeprlQHWbiuLHCpMT0lqhItv1RrNXPMSz/jZ6P42NfitXH70oaijwv4uDmuhOzZ07B86CnsDRvJXzEb9++RrOmTbB0ibPMCdv5Ye5knlR9sU2ebJPPwP74PEz/4Sefdc6sqdi4bpWMhyk0jOxVpePEwOfmVUyeOBavX72ML8/+2I4yV2xDnkVp/+vcqPpRSdLFMX2IFm4idVn/33AyKlwTgJTkGvPDjXWrVgugkSLuagq5oqEhIWhnaSVWKdM2SNiSnuPoocOS50iJsiOHDyfb1I6tWyVtp37durjt65ts2cy+SV5aEhzwJcDXJ3m9V5XqzCCZM7PmLeDn6yspNQwa0uyJZt5fKtPBlEAXEvwMlEEbN2YEpk+dKHJpJCE4c+o4PA64YsWyxRg3ZhROnjgab20SDLm4e3q4YcrEsRg/dhSOHPKUPMvAgEdYutgJY0c7Yr+biyzYVIAZP3Yk5s+bhYf37whAHPbywNhRIzBrxhSRc4sVAHyPUyeOiOrMQqd5ePzoHs6fPYlLF86Ku9Vl93ZMHD9agIbE+ZRJE4DY8gAAIABJREFUI7dv2dKlhFvX58Y1IVBw2bMDY0aPxMoVy/DyhYq8gc9KqTMSygc8uidgS+UX6rjypYC0iscOe+L61Qs4cfywMBm1a9MaK5cvxbu3r2FmZooVK5bGg6kydyRnGDfGUdiY2Mbn2I+4fu0Spk+bhMmTxsH70nm8DX+FrVs2YMyo4di4fg1evggCRdiveJ+XF5C9e3bKPQoM3L1zS56xYrkymDFtMm7dvCYgyv44F0cPe8kLEEHy9ImjuHjhtHAqcxwjHYdi5fLFCHn5DFER73DU6wB8rl8WEgrfW9dx0NNdpO0of/dvAFI+owZMU79AcUEf0LsP8lDVpIM13r19l2zlfXtdRTqNjDlbt6RMN5hsYwD8/fyEZIDW6ZpVq5MtfuTgIRGlrlqpMki4/qsOUvJ1sbGVvd7uXbsiMjL5yPEH9x+gsYEhiuTLD46dxAjW7dqjf58+oJzZIicnidQ9cuiQiI8/fPgw2f3jX/Xc/7R+Mx1MaeVRF9Rp/mwBwPZtLNCtiw2CXzxHj+5dYdioIZwXzkPr1pYwNjbE04BHYm1xYacbct2aFXBeMAfDh9rDyKARbt+6iQnjxqK1pQWWLFoowHXs6CG5R1WYZUucheP3oKcH9PUaYtzokcLha27eEoGBj3Hs+GHo6+mib287oQ2kZioVX0gjSBah5UudRSaOijSUdbt+1VvKFi9aBPaDBuDypYtYv24tLC1aYfGSRbC0ssDkyRPiLTqyPLU0bY41q1cgLPQltLXqieg3Ld5hDoNA2TWCkY11ezDUnIT+K74D0yXxYMq544vBQqc5WLp4ATq0ayPk/JzPtm0s0bdvH7Fqz545hbVrV8v8kdN447q1ePzoIbp17SwUgwR0g0b6mDNrBjasWwOqyPTs3gWlS5aQZyLLkuIl4H1jQwO8CAoQgLeyaIU1q5bBeeF8UfuZMW2SSOGNdByG0JBgtG1jhYUL5su/+1WrVsDEpClCgv9d2qkaME39svfo0SM00m0o7lNGlyZ3REZEoF/vPkISQIL6p09TR6eXXJtM7TAnE1KOHJg8YWJyRXHV+7JEwpYpXgIE9V913Lp5Ewy84v7n4sWLUxwGcz4rlC2LkkWKoUSRoirKvuw5xVLNnT0nCuTKjaL5C4DPVbViZeg30MX0qdOEZD7FxjUFkpyBTAdTuisJjA/u++HypXOYNWMamjUxgp/fbXTp3AmjReUEOHbsMLTq18O1q5fj3Y50OQYHPcXli+fgsmenkMgf9DwgiixmJiY4f/Y08CUWZ08fR706NeG0YB5CXz7Hl7hodO3aSeTb+OSBTwPQqJEe1q1bjcGDB4qSTNznWGEq+hT5HnY9u4n6CgW2g54+xqUL57B65Uohh/e/4wvPA/vR2MhQXgA+fYyEubkZunfvBm/vixg4sD/MzFoI8Cuu6AnjRwsXLjl5mzQ2gqW5Ga5d9Ua7Npbw8nDDmtXL0LaNhRqYLlGzTL+BKa0ezh3B0/vCOcybPRNNGxvi2tWLaGVmgv79++IOeXsBLFu2BNo69bF3rwsiP7zHx8gItO/QDjNnTBdS/fr16mLThvWy98vyu3ZsQ0uTFgh+HiQsS/QC0KX75PF9NGvSGAfc3XDh3Bl5oTh+7LAA6cb1a6Wvg14e0Gugg/PnzsK6Q8d4MF25crkGTGWGNP9JagbcXV1FK7Ry+Qoi1JBUOV6/fOkS6tSogYL58mFOCsCbXDvq92jl9elpJ0xI/Xr3Tja/9f7de8IZXLhAQaxds0a9mSz9vmnDBtkzJmXfhfPkA0/6+BwXh/HjxiJ/7txC1rB08WI4zV8gPLm9e/QUhZ4mhkaoX6cOqlSsJMQNTBUqXKgQnJ2dwchpzfFzM5DpYMoFmnqcBBK6CG2tOwpJvP8dP3Tv1kVclxz6sWOHoK/XANevecuiTmAKeHwfdj27CtgNGz4U1apXARf20JCXIllGYe2RIxzwKuwFDh08IET1tCYPeh1Ah45tMXOGioPzw4d3MLcww8yZ09Gpky2mTPn6RvolWiyyXr27Y/yEsbh/119UapiT1bdXb9SvVwd3/f1EWo0C26/CQvH+3VsByDZtrTBv3hxMmDAOWzdvEBcxXdOk+GOAkXmrliLnNmPaFDgOG4oRw4ejs60NwkKfY/WqxejQzkpAvmP7dlizeqW027KlCdauWQUgVlykqrk7jrZtLeA4bAi6drIRS/5F0BPc8rmOAQP6onFjQ6xetRzv3r0WnmIz0xYiY3fr1k106dpJrOa4zzHYvnUzrCzM0b6NFfxu+2Cvy260NDFBWMhLealQ9mdJgTh54ng4DLHH5IkTxMVNrt8G2vVw7Mgh+VdGGTsDfV0c8jwgf89lS1Rvy+vXrxN5O7q9aVX/a9y8GtKG1K0+X76oRL1z5hIC8+AXyUuhzZ89Rwjt64r81uXU9ZFkKZXiCEngp0yYKNZuawtLhIWGJVmDlnBjQ0Oxon+VFFtsbCyGDBwk/LZtLK1EgSXJAQNCqmDWwkRSeobY28cX5V4p2YjehofLSzwlzs6cPg0ySbVt0wa5c+ZEuzZthZc3vpLmS5pmIFPBlFapLM6TJsLQoBGePwvAYucFMDJoCL/bvrIQjxszUgZ8+OAB1NeqjetXLwqYElA8PPahfIUyOHXqBM6dO4OaNWtg3769ePU6DO/evcG2bZtQvfrfOHrUSyw7SpyZtmiOoUMGYeKkcbCyNMezwCc4fNgLWvXriHbp1GlT0MKkGW7duobAgId4FfoCPXp0xoQJY7B54wbUrFFNRLz37NyJWjVrwO/2LXh6HECdmtVx9vQphL95Je7TPn16ITj4uUi2Pf//27sKsKqSL/50TfIFXQaCnWB3K/1oRTEQO1ZXd//mGrv2GmsXoEiqWKtrrGJ3dxCCDUgKCCq//3fmvotPBCR012Xv+77h3jd37syZ8y7zu+fMidjoXOAgEEmIf4HevXugVq0aOHLkENavXQMdLS2WnJwmu3zZItjbWuHJ4ximJp01cwa7p1fPHpg962du/5KAGdksw0ub1s1Z6rbVK39Hm1YtcPf2DcS/fIakV3FMbdy5YxtERdxje8wUgN/CoiE2blwDT88+mDLlR6QkJzKJ9PbNG2jbuiVWrViK/fv2ok3r1jh14jjrh34rAr+c99k4f+4Uyw9L6e5oX/t1WiIIpGmP+FVCHJb+tghdO3Vg6nRXF2cm1dOLxo8//ohOHTvi5fOnApgW69/wv9GYYuva9uoNsao6k5QIKAr6cHF4rZkby8ihw5CRkVFQ0yLWf0jftX7NWsg0JejQvj0ePijYx/X58+ews7KGVE0dc4oQHL+IhBSrWdTDCHTt0JH5i/4655ePXIby64hCH9auWQssO05AQH5NPqlb8ttvLDhFj67d8MUzzHwyWtmt+KpgSoszWbuGHzkMG6teGD50CAYP9GTHyIiHbHFe+fsyICeHJdzu19edWc7ylqqUFLufRx+4ubqwfKFOjnIcPxbOjH5oP5Dyf06b9j+Q8dHIEUNZ367OciYZRkbcZ/uL9nbW6NWrO36eOZ3lMr3/4A48+rmjR/eu8PYajAvnzmDer7OxcsVSNraT3BaDBnhg+FAv9HFzZom5Yx9Fw8VJzoD64J/7cP7cWTg62MPV2Qme/fqybDOkjiXJlECJrF0XLZwLOzsrPH8WizOnjqNt6xY4sH8ve5L8N2/C2DEjkZKchPlzf4WtdW9mIPXLnFmgPco7t66xFwqSTMOPHGIq3aFDBmPI4EGMd5TSjoytiE43FznWr12JHaFBGDywHzz7uWGY9wDcuH4ZU6ZMwtq1K3As/DC8Bg7AoAH90N/DHVevnEdMdATI+IlAkrLk0EsA0U7+tNlvMuAkt0en9m2ZMRGB+v59u2Fr3QsuTvZsz5Qk3Zx32VixfBnLMUt70P09+2HkiOFIiHvBjLQEybTsLhwlmVn4kSOoU8sMlAFlW0hooV3s2r6Dhbyjfb2SxOEtrPM/9vwBI30jNKhfH6dPc6ke82tPlsP9+vRl/rATx0/Ir8lXr9u7ey+qGxqjpkl1Ftrv0wFJLfvhRWHhvPlMkm5haYk7n3H94fuilwuK0dumVSvQnrbwKRkHvjqY0gKdlZGGe3du4fTJY4h9FIXnTx8zSZJcQ14+f8KsQpNevURsTBQLxM5LSbSwP30cg5PHw/Hw/h28eBrLghyQNW/4X4dw8vhRJlWRipIMhcgK9cG9WwrXlgzWN+UwJWvXtLRkZGSmIfPNa8S9fIbTp07g0oVzTGIjtSRZHBOgkGqZ9mCjI++xlHEUGJ4PxH/x/Fk8jX3E9hhpHkcOH2C0PXsSw4CIBw8CVTKeevo4mtWTT+fjmAg2NwLd+JdP8SQ2GhlpKUhMiMOdW9fxKv4FM4AiqZOkZeqD+qN94wf3buPMyeMsJRvxKyUpgWV+OXTgD1y+eJYBF0mqZIl75K/9IDUwucg8fhyFly+fMDU47bkSf2h+pEIna+rHMVG4dOEsMzbix6P6uBfP2csDy/f6PovRQi5GZAx1+NA+3L51jaWyI74kJyawPWbKP0tGZfTbUqq30ge4+Pe41ZS1QPdZ2dl4lZSIuMRXX6Ak4lVyMlJTUzH/l1+hJRajXevWhfp4Zr15g3GjRkFDVQ1du3XDzTt3kPw6DfGJiUhITEJ8YhLi8i1EMxW6/in98UmJSEtPx+HDf6GOeW2YGBlh186dBa6cygZQgwcNwsu4OCSmpuTb95fh1Qd+J6akICkpGZN/msxUtt27dCkgFRwBKQemKcnJ6OPiylKfDRo4kKVTK3ByShdCAoNgom/AEqcfP3USKenpIF4VZ07st3mViIQvWOJfJYLKl+zz8329QkpKSh4Xog88VmLbJ6dfHUwJEAgcKRoPpROjhZyXguhIYMHv19E1Hkh5YKI68pGkI3+d7uFTjxE40T0kxXFpzD74qpL/J9WRbygFjEhNS0QqAVRmGrufpGYCEeqPCo1JUjH1TbRlv+GkNQIGktbev81iBkHUjmihvpn/Zj6RhIguakNtaQzql440V+VrBF5EO08Hf87Pn58bSbvUH887OqexufYpDBypDd5ztNP9NE8+UAZdI5U7TxM/B6onevjv169eQr++bsxamNyG+Pbcb5jO8Zj5A6ewYBZ0L0dHJrIy0j/yueXnUOaPZSycYEpSCu5cvYWbl2/gxpWbpSuXb+DOrbs4d/os7KxtmLpyxNBhSH+dx73jg3CFa1euoFUzS+hpamHy+J9w//Z93L5+BzcVtFy/ehPXP0MXXc/bhu5/ePchDv7xJ1pbNoeOVIY1q1Z/sijyFZQknCRSCsknt7HDpbMXcPfmXdy8covRcvMq8ecDj6j/vCUv//Jep+/Kbfjvd2/fx/FjJ9Gtc1dI1NXxw/jxoKhMhX04g61GTDJdvWpVYU0/unZg/58wq1ETZtVrYKvPFkTcjcAtfo75zCnvHHj6r169gcvXbuAyHfMt13H52rXCy9XrH917hb5/7p4vcp3GuYFLV67i3r17IF/dD5+Ppf8P9R+f/S1gWuYX0zISnIBeKGgfeVfYNty9c4MBd9F+Oz6H7L9HmizavIo2n7IWASkr/Q0SY+Pw6lEcXsW8LHFJjHmJhJiXeB2XjIP79qNhvXrQ09bGhrVrP16F8nxbvmQpdMVStGliiVMHjiE9LpX1Q7TExxZQCrvG3xPzEinPkhB9+wEXCEJNHVP+9788UsjHxMyZNQsaamosU82D63eQ+jwRCbEvkBD7khVlevg65aPydTpXvsaf820+8DoOqS+TERa6g4vHa2CAgCJEPdq4dh10ZFqoX68ezpwp3OpXeZZnz5xhv42JngGCffyR8SKZ+/1j4pAQQ8+AcsnneYiNQ1zsS8Q+eoJHjx4j+tFjdqTzj0ssHsU8Krw8imH3831EP4pFdEwMHrHyCNGPuPLZfj43Tp7r0Y9iONqjY1iO1bdvlWMxK73pKTMuz7kApmUECL8UOPCSJknSX6rPst5PmQHTHOA9cvAyOxWXUx/hXGokLqRG4mJqNC6UoJxPjcbFtEe4n/4cC1cuhb6WDpo2aIiL58/nWYY+fH35/AXk1rZQV1eFlZcrTiTcxeX0WFBfRAMdqZwrZqF7zhI96bG4mhgFj5GDoKGqwhKFU0qxgj5LFi6GWF0DLXp2wIEH53E18wnOpUaViIbCaObnR20uvo7BnbSnmD5/NvMtpTRpt28Xnu+VAjmMGDKUJRHv6+5erFjC9+7cRSuL5pDoSDHbdwkuZsXiVHokTqVx5XRaJM6kRuJsahTjIU9r7jOREoWbKY+R9vYNp3HmtaL5HnOYjQzZyRRU+EukvebOc0+A90rnfMMvcaR+FQKoclzogp6L/OrLMJj+t6Wlsg5g39T8yoKaV7HwkX1t2JMLaLljPMy3D0XDHSPRMGw0Gu4oemm0YzSo1A0bBYud36PX5kloZNUWuhpSeLi4ISWlYPDaH7Yb5tVqomoNbVT7yQaN/hiPejtHMxr4fhuFcf03ChvDxvmkXjF+br3ie72wUai/cwy6h/4Pjft3h5ZEDOvuPRETW3AwiA0r10BfSxuqrWugybohaLz3e1A/efv+Ut/r7hyFJrvGoaffBNTp0QI66hJ4eX5+/5NUkxR8gQIyDPMagksXLoJyslJGnscxscxlJikpie1dE/BmZWWBt6aOe/oCPTt3QxVdDZiO7Q7LPyeg0b5xaLR3HBruHYMGe0ej/h4qo1Bvz2jU2z0adXePQp3dI2G+eyTMwoaj/fYfcDD2Sn4Y85+p+1vAlPbb+H25kiyCtG/H72kW/f4EFsSd9iL5QnQU/X5OxUfj8vuGn7uX+i/JWLSPml/f/D4lhe3L77pQVzQ17FfnU1kB0/dktw2siw6Hiq8cIt8eEPlZl7xs6Y3Km51gOMMGsvrGqKGlh2WLfitwcSU/yInDxkAmlaJCxxpQW24PUYAdRJutufIRLTYQ+dlC5EfHotBoA9FmK9aP1NcdJt6doKOnjdZNW+DWzVsF0uS/wQfGunoQtdRDxYU9INpqC9Hm3sUYl6ctPzqtIPKjwrexhsi/NypskcNomhUk9YxgLNHFmhUf73/yAp8y0aGhoahpUo0ZEpnXNGXGRJaNm6J9y9bo1rETU1O7OTrD08ODRZWa8P14TJ08GYsXLcKiufPR2rIFJEYyqFvVhvq0Tqg6uyuqzu2Bqgt6ouqS3qiywhqVVtug4jpbfLfRDiJf+l3sIPK3h8jfDlU2OGHptV3KJP3nzv8WMKXFjCxai7KoKRvoEJhQqDayQKXwdhSkvih9cG0SmFEOGRmR1SkZEJHBEAvKXkTVLo1HsXajIh4ww6HCLFR5wyUCU3pxIGvaotDKG1+9zfrY+IrmTpbEkQ/vMEtj+l6U/oQ2/wDAliEwzSIwjT0OjUAXiAKtIApyUBR7iIKKUQIdIAqxhySwL4xHdIHEWAsWtevjzKlTBS6yly9cQrvGLaCmJ0EFr2aoEuACUYgdN26g0th0Tv3ztClfIxrZdaX2fF2wHUQhNqgU4gK9H7tDr5o+GpnVw6kTBdMU7LsF1fX0IWqhi0pLeuG7bXKIgm1LxpMgO4iCbSAKsVYUOrdR9Ef0Es/soBnkDpNhnSAx1ELT2g1w7szZj3iWF0wpIAMFhdHT0maAWrNadZZdhiI3UXQjqYYmJGrqLB6yZlUVZu2rXlWFGVaJ1dShpSmGoY4ODPR1oFFNCxXryFChoQ4qNtNHxZbGqNSuOip1rolKPcxRxbYeqjg1ROU+jVFloAVUvFug6ujW0BjZDm6/jMXeA/tx7tQZZkR2//49FiDi5csXTBvxJjPjm4qwRHz8kp+vDqYEMpcunMHY0cNx++YV5oNJwECgQ5apdFQGAPpObioP799GpsLSd+TwIcyXkgI58Nas/D18P3klX5ImyfWFAg38NGkiC0G42XcjczvJVkTnIYmP+lOWeok2vo7CEpJPKMXpJdcdyhSTdxyig+YYHXkfC+f/Aq9B/eE9ZAB2hm1jlrTUN99fLs1pXCo3op36S4h7jvt3b+VaNfN9kjEQ+bpS4Pyc3Iw5XH88uNKxIH4qz4UfWzh+BbAtC2BKq0oOQGC6IeY4NAPcIfK3Rbmtcoi2OkLEjnRexBIghyjICUbr+qJ6LwtoSSRwd3BCQnxCgevX0vmLGCBUbqSHKvN64LsQF4gCHNiY5UpCQ15aAxxRzt8BKr5OMJnUE4ZmxqhlXAN7d+0pkKbQLQGooW8IkYUOVBb1RsUQJ4gC7FESer7b6ogKvg4QbbSBaBMVa4h8rCHyteIk8EA5ygU5Qn9DH9TsbclAztXBEfFxcQXSRxeePX2Knl26sv3V4UOH4vDBQ9ixfTt8fX2xcuVKLJy/ADOnT8ekCRMwavhwDOrvCVdHJ9haWaNb5y7o0KYtWlk2h2XDxmhmVh+Njc1QX68mamubwFSij2qaOjBSl0FfTQZddRm0NaTQ0pRCKpFCpiWFRFcGsYEWdI0MUMvUFI3q1IVlk6Zo26oVunbqBOve5J/uiEH9+2HUsOGYOH48pk+bivnz5mLFit/h6+PL/I7379uH48eO4eL5C7h58yYoaMbX/HzpwIlfHUwJSChLyZpVy1mUHpIU35NLShrlCH2GNxkpyEgn9xhO8gLeYVfYdgwf7o2kxDjWzt62N35bNB/v32UhKeFlLqDyIEV5QQk4eHUsnZPCav3aVTCtXg3z585lwRHat22DCd+PRVpiPN6+IQObZMQ9fwoyIKF7CdwIGMlHlLLF0I70iuVLWGADijZEkiK1UQZfak+gR8EeenbvjIXzf2VBILZtC2bzIrAk+lKSE5CtMOqh+98r0rRR4AOKrDR4wADm90n9kbRKLi8kEVOwBwJ0mg+50VDoRKKP6OVANIW9oBBd5J9KfdOYVGi8V3HPkJYUz9LcCUD6FYCUtBxlCEzJIWBdzHFoBLgxMC0yeOYFrkB7VAlyhfFcOQwa1YKBVBeL5y0qMILPs5jHcOjeG5W1VVDJsTaq+jpDFOxUdPDOO37e7wFylNviAOPtQzDo/CqM27IA9Ro3gKG2Prb6FZyNJtjPn5NMLXVRdXFvVAzlwLT4fLGHqr8ruh6aiWFn12LYyZUYcnIFBp1ZgU5HZ6ASe1mwQqVgFxjPd4JRE3MYiLUwd84vn8UT5tpSvQaLebx3T/4vBmRUQ3F3aZ+U3D5SU1JA6e0o2caD+/dx/do1nD19BuGH/sL+XXuxIzAU/pv8sG7FKiyZvxC/TPsZkydMwrhhozGk/2B4OPeB3MoOvTp1R+dW7dC6iSWa1G0A8xqmqGZgxHLVakukkKprMj9ZTVVVLtC+qhr7LlHTgExDEzoSKfS1dGGsb4SaxtVQu6YpGtSpi2aNGsOqZy+EhoTi3bsvDXscS/91kikBHsV2Xfn7UlC4vwN/7sPcuXMwbdpkFiHolzkzkRDPAeTbN6ks+o+NVW/o6+tizKhhuHD2JAYO6AcXF0f8+OMEONhZwc9nPd69zWSBD2ZMnwJXF0eM/34MA20eZAh8Vq9czsLeUQg8+vx16CAaN2zAAj5Q4IcJE75n0ZUo4D4FaiBafX3Wo29fVwzxGoiLF87Bz9cHbVq1xPTpUxgN48eNRmxMRC6Y5bx/w7LhtG7VHA8fKJIN52SzsHz3793C/36aADdXR3j0dWOhBSl4hK/PBixZshATJ36PrVu3wHuIF0glM9DTA+FHDubmYI2OfIjOndojNDiARSZavnQxo83JyR7r161i9O7/Yw8WzPsFM6ZNZuETZ8+cxqIWEdBSBpy+7k7wHuyJ40cPsRcFAVC/AqCWITAl789VMUehGuAM0VabEkqlnApW5t8XRiO7QttAD03N6yP8SHiBwLAjIAS1DEwgMtdEuR9bo2KIG0QkqeUFxZJ+J0l5iz10gzwx/9FenH9xD11JmtOQYnWePUllIjkwNYCouS6qEJgqJNOSgGlFPyf0/usXHH8dyfam0/EOyXiHG+/iMfamL1QCnVCV9pjH94CWkT4aVDfH/v37lcnJ9/yXWbMhUddAhzbtvm4Eo/fvkZX5Bq9T0/AqPgHPnzzFo4hoPLh9DzevXGd+uCePHceB/fuxPTQUFKB/9YqVWDBvPqZPnYrx476Ht9cQeLj1gaOdPXp1646ObdqhRTMLNK7fELVNTVHd2BjG+vos3y2ppps0bIRTJ0/mO+9SV35hNP3qkik59BNAtG7RHFcuX8bsWTNhYdEM27eHskDxdevWxrFjR7hgAxnJoDi3gwYOQJPGDeHvt4llTHF1c4aNjRUOHNiHcWNHo2OHdoiOisSsn2fAs38/nDh5HJ6e/fDD+LG5khmB6ZpVKxgYPX0SDZJ4Kcxdty6dsfL35SzR965dYTh+/Dgouom31yBQ/lILi6ZYvnwpjh0PR3R0JDZtXI8G9eqyAPR+fj4gekldTCpnkgYJvId6D8LgQf0ZwJNqmgCL9kyjIu9h754wnDwZziTtvn3c8Dg2Fo5OcnTu3AkrV/6OM2dOMFV0vbp1GF3k35mZwSU7j4p6iE6d2iMkaCuTSg8e2I+jR//C0mVL0LJlc9y7dwuzZ85A44b1ERIUiOXLlrLYwieOH0VoSCCsrXrg0MF9mPnzdMgd7PD8aSx4FbcAql8OVMtMBKQc+i8Bgp5dgFZAX4i29Ob28QiIigNigQ4oF+wI43V9oW/TFDKxFG62jnjxgrQ9n35SU1Ix2GMAtDUlqG7XCuZbhkHk7wBRoB0D1HzHJpr4UhTaWFsHlPO1Q90tQzBp13K079IJOhoyLF205FOiFDV+6zbCSEcH5VrpoepSa1QMdWZq3lyaikoDtfOXQ22DE1y2zcTVp4qYwMTwHOAFMvFz1E603DIa+nJLSKQS2HTpidjY2AJpowsJ8fFwcpBDvUpVjB45CpQv9lv8kESc+eYNi8pErkivEhKYhfGj6Gjcu3sXV69cwemTJ3D40EH8sWcv1q5chTZkFKVR1NJJAAAgAElEQVSujp9nzMC7QuI4l3i+/z4wzcLx8CPo2L4trl69glkzpsNrkCeb/6PoSHTo0A67d+1g2VZep75i9ZSFhPKVZqansSg+cicHLF68gF07dOgAunXtjFMnTzD1q4eHO/y3bmFSY7++rkhJjmcSG5NMV69gYPTkMYHpWwamPbv3wLIlv4Eyqez9Yw82bFyPQYMHwMVFjtu3r8HZ0QEUI5jSjNHSsm7NSpZCjdv7fM3Oly1ZlAumWW/S2HxoX5VU2KReJaCiI6l2KR6vz6a1LNavg60NIh4+hLOTI35bxM2HJhUaHIiePbrjxfMnLH1crppXAaZBAZuZypn2gCmrzK+/zkZTiyY4f/E85sz+GQP792O8efY0Fl07d2Bq8gkTxqJT5/agF4Dvvx+HDu3a4v6dW8wISwDSLwekxMuy5GdKD1Ls2zRMuhUEzYA+EG2x4ox9Aoq4b0qgEeiAisEuqDnXCTpNTaEr1cbcmXPYM5rfHwrO3sCsNoy1dLF43QrsSboFywM/KfYSbVGOVLRfZM+UaLNH+QB7VFvkhBot6sNAoo0l8xbmkkXrq/Iau2bZCujJpCjfzgBVf7dBhRJLpgT+tP9rj+822qBl8DjsijrHXl54f8oUvMf2K4dh3qYhpGJNTJ3IJQHJJS6fE4otXK9OHehr6xQpsEM+XXyzVSO9hzFjKS+vwUUOjfhPTuarS6Z4n40Tx46iU4d2DExn/zwDQwb3B5k6PLh/myW03rt3V+4iT/uUSxcvYgHf32VlICsrHRyYcomEKdtJrx7dcfLEcfTq2Y2pgvfu3Y1tIUG4eP40AzKSGAk8Kcdn165dkJzMgfTFC+fRrGlT/LlvH3x9NqJHz27w89vEUrw5Otiyeylu7+/Ll6Jzx3b4ffkSbFy/DpSqKTU5EcmvEiC3s2WZb6h/UqWSmnfO7Jno2qUz4uP4lFJc2C+fDevYPqqfzwaMHO7NXhAiIh6yAPnLl1BCbfLqy4b/Zl9079YVrxJesjqyfKY904jI++jYoQ0LMn/n1lV079qR5YMlILawaIJLly+AguNT8oD3b7MRGfmQ5Yrds2sHxo4ZBRtbK+zZswvbQoNYwHwyoqJ+ecAXQPXLgGpZTA4ehywsfvQnjEIHK0CNgMDx86BGgBHoAI2APqg+uhu0jXTQwKwOwv86ku86R/t4U378CTI1dXRp2Q4RdziJ7ejrSPQ4OhsiX3IXsWZ9KgMqqX/5wiTUokin1IasgEPlMFzmDKNWdaEvlmHxnLlKe7k5yGHe+xy5y+YvhrZUjO86GqLqKlt8lwdMc2mgvulFojA6FLxh1sabbFF78xBsuH0AmTkfsueQkGBuZgoTQ0MEbPFn/qAkbWZmZCgKnWfiTeYbvMnMxPKlSyETi2HZrBnOnzvH0qx9aMvf87njh/4pO09+hcb8UD7X34fr+fWlXMf6pPkpFfKBfRWfiP7u/aCuooLhw75E1qB8H78vWvm3gCllPmlh2RSXL13C1CmT0c/DlYHpvbs30bKFBXbt3P4RmFLKsqZNGmF7aBAiIu7B1t4mNzfp3l1haNe6Fa5fv4pJE8ehj5sjzp09xRKFUzYVAggeTFevWoFatWph8xY/BAVuRe9ePTHQsz/SUlJYvs6ePbvj4sXzGD7Mm2W1oSD5586cxKULF+Du6grKhELJdXv16MkCuie9ikfP7l3x26J5DPRoLAKna1cuok2r5hjQ34NZ8YYEB7C8q5Mm/oDu3brgwvlzGD9uDHr16IaHD+7D3s4GCxfMZXlEkZPNJMlGDeoz4I58eI/RT/3S3mzrVpaYM3sGtoUEoEG9Oti5Mwx+PptAauELF85h+rQp6OPqgndvs/Dw4X1Gx86w7axNl84d8eeBfTh37jQLiE+ZYubPm4MTx/5idAtgKoDpJ6sJiWVk7/EeoKRnm5+dQYNd4yDyIYtTW4V0xVnY5gscgfaoEOgI4/UeqGZjCQOxDLY9eqKg3KWk4mvbuhXU1FQwecpk4N0HufDqm+foc/Z3VN7iAJE/Scjkd8oBFg9idMyXjoLqA+QoH+yE6svdYdKmPsuXOnfGTCWXDSXZNCcH82bMgkyiiQpdTVB1rT3Khzh+pObl6SguDQzU/axh6NMf8y6GIOUdp55d/utCGMt0WcYcJ7kcQ72GYOjAwRg5wAtjPIdgrKc3dxw4lJ13b94OZlqGaFWnMYa7D8C4gcMxxtMbo/MUqstbqC++0LURA4bAe+BgeA0axAptf3kNHIxhA70wynPIJ/fTPfz9/JHqRg4gmr0wZNAgVqg/1peiX76eP/LX+TbeXt7o49oXtU3NQUZMK1esVHrZ+eSJ/WYqvjqYknXqrRtXMHPGFFBatNDQYKxdvYJZlz6OjcKsn6cyiZLa0eKe/SYdEQ/us5RrZJBz8sQRrFy5HLt3bUfO+7cMFGb9PB2k0iTgIZcbSkM20LMfwraHsD1TAtO32Rk4fvQv0D7lgAH9WCJykuJioiKBnPc4e+YUBg8eiJGjhrE9SzJWIunvfz/9wFK3jRjqjYsXzuLI4YNYOH8uy4RC2VoWLZiLfXt35YIRSadEOxkweQ0eCGvr3rCzs8b69atx4cJZeHt7sfRwUyb/iGVLFuPZk8dYtvQ3/LF3J7uPgulTnldy33FzdgLldSWfU156JKOjcWNG4urlCyxZN6V8mzF1MqM5IuI+toUGY9WK5XiTmY6nT2Ixe+Z0XDh3iqmYf50zk5mke3j0YWnrIh/ex9jRI/DHnjAW/F8AUwFMC12J3nOuMgcS76LdwalccAEySqK9zK0FAGqgPaoGuaDGfCcYNDODudQQHZu0wJTxE7F6+UqEBW/DmWMnERMZhcy0dKxbthL6ejqoaqqNfr//Dw8ySDujAHQA0e9SMeGmP8SB7lywBJIseZUvAevnpMG8oBrooABTN5i0awBtiQQzp0zLNz7v+7fvMOunKZBJxSjfszoqrP8UTNn4xaWBpylQDtFmG0g3uWLSqU14kp6ISaPHQ0tFA/oybeYfqq6qBnVVVUg0NaAjkUBPLGVxi3XFEnY01taDqYExaugZwkCiBV1NKXTEMuhpflx0NWWsnq5R0VVuQ+diGfQ1taCtJmb5ZsUKq1tmhauuBi2JFDoS7j66l85ZPzSO4n46smtiGaRqYmiqqissedXA+iGLXlXuXKyqyq59GIfqFUVFFTRviaYYjg5yPIp+VOhj+q1c/OpgmnfBJpVYuqLw12iP8MM5pR1LY/tQlG6MC/bAuc0QcPHt6MgDcFTEfeZWQv181CYlkYHKq4QXLHXbu7dvcu8hwyFK4s1SoSnlIiVVLYH0q7jnDNS48Whcfmz++GEhpjEp+0rG6xSWjPzZ0xikpb5C1pvXTHVLLw3pLBIT7adyfdE+Gz8X2o+lFwCab3JSfO4cqF8CVXLToewvdG9sTCTjCbnJUB8f+vlAF/GBDKAoaw5ZLUdHPUBKYgLS05KZpe8nfCpiEAueXuH44bcjXpQlNe9b5CD+TSpSKAsTfSgUKoBzmU/gfHoxKvhR1BuSUAuQCBVgajDfHuJWNVHNwBDVdQxhqCFDdbEe6hrUQIvajdCrbWd4yt3QrXFryPS0UKl3bchWOMF2/yyci3+YOzadxOENFkbvg1HIIC5iENt7LGB8HqgKOtL+K0mmK/ugWocGTEU6c+p0brx8/s74cTJkYgkqW5mh8lp7lKPACvz4JQFzZbrofno58LeF+gZHDD70G37fvRkTx43H6KEjMWr4SIwaOQp9hg2Eqbw1qljXgYZ9fWjYNoCGXUOu0LltA2jaNoSmbSN21LDj6lg7xXU6V7f7UJTbqNnVh7p1fTRy6oQhY4Zj/KixGDNiJMaMGIFho4ahXX9raPauDxW7+lCza8AV+4bsqG7XEFT4enWb+qhm2wLuXp4YN2oMRo8YwZXh3HHUiBEYOWIERo0Yznxeye81vzLceyhmz5rFMrjk87N8k1V/C5gSKBBgvCZwID9NhZEOgQPVfwSAioWd6gnwCGQIUOjIgwDrS6kdqUQJWKmN8kJP4EHuLtQPHfNep3peCqQxiA5qR/3Rkfqie/KOl7cffkxqR3QwWhRzJOvZvGMw39o8tCrPl++PH59o4YCV65/a8vQSLXTO30PXqC3xiq4RLTQfvk1+fODvFY4fg2RR+VFmwDSHU+3ujD6P6cc24k7KY27RUrj5PXiXiBFXN0IjsA9EW+04UMkLqgEO+C5ADskmV+jM6AXdgW2g62AJnS71odXcFNLaxpAY60KiowVtsQzGUh2omGuh4sS23H6krw06hE7Cn1GXkZ3znkNyBV1bnp1Fg73jmItLLqApg1NRzhmYOqLmqr6o1bEJjKXaGNrHE0f+PIRjB48g/NARhB85ilOnTuFYeDg83foyK+O2A6zR78QSaIT2YeHziqXWLZQuR0XEJiumSrfZ/zNOv7iD9wq/0Pfv3iMt+w38HobDImQ01DfIoeHjAskmN0g2uUC8yVVRXCDd5MakXK6ern0oVPdpcWV11J/GBifU8RmMJRe341V6CvNHJQvc7HdvcS0pFoOOLIG2jxvUNjmz8alvTerfhxuDzjV8XaG20Qn11w3Gnw8uIIf8WrOzQRlYqC/WX54j7ZmTD6yyH2x29ltkZytnbfkmsfMTov4WMC3qoiS0K9liLvDtn+VbWQLTNPIzfXgY9dYNQFDEMW7BINFUkUrzr4xI1N49SmHlS3uYH6t7+T3M8oFyVAlxglqwE9T8naG2wREay2whm20FnYk9oD+kE/QdW0DSrR4qejRE5bV23H5kkB3Kr7bG2P0rkERZSOijAPNXyMHg6xtQzo+kQ/vi7ZXygEZgGuSAamv6olqXJqimo49GRrXQuGYdNKhpjnqmZjA3NUWdWmaob1YbpsbVoKWmAY9Rg7El5TwM93pDtIUMopQk47wvFPxYRTky6dSehV6kUIKyDc6Ydz4IWR/ZFAP38Rq9Ls1nsXspTGP5INr7JZW1o6LQOV9H53mLPcoHU+HrqS3dq7gnxIHFG+4UOAEPkp5yfFf8pZ9/VeIJqG9zhyjQFuVy++DG/I71IUd56iPEngHqkssFJ13/qPMy9EUAU0HFmSvVCqBcMlAuE2CqsLshMPW7H47xh1YhIiOeW+oIzHKAeGRh9FVfVNlMUYBIMlUClLzAQXuToY74boczyu10hojKDmdU2OYMlVB3SALcoevrBu0NzlD3dWZGSyKKXetnjdpbvfFn7BUOQ4kuhYC699VNmO0cxQVYzwPiRZYUieYgObR93aE1oBXU6xlCz8wEslqG0DTVg9REByYmxjA1qc6AtHYNU9Qxrw3P335En2ur8F0oxQumOLoKFS2BKgE0FZ4HhfGFb6OwRqaXETKI0tjqBq2Ftqg+ogscRnrghwnjMWf6LOwI2Yaop7FY//IEtHYO4AJpMEMszmqaqYlJVfzZohSzOG9bil0cYAeNjS6YeykUb94ppELFS8zt7DhYH/sFoi20X66Ii5y3D/ad+GKH8hts4fXHYsS/USSAV/Sj/H6QlpaG8PCjWLRwIX6YMAETJ0zAsiVLmUaArH3/jR8BTAUwFcC0lM9AmQBTWr1ygDfIwfWER7j76rECzLhNU4rZu+nJKUgD+zOjmQKBlAGJI1T8XSFdbA/1SV2gMasn1JdxEotqoBOqbHNChR2OKLfTgSsUPJ72IrfaQWuDKxZfCUM2v/ISmAK4mRWHnkfmQORDUZk+loaLDKQ8kAXIUdnfGdJVTtD6uRe0p/aEbGpP6P7QFW5zx2DPvr04cugwDv75J8JCt2PVujUYE7gQNZZ7QGWVHCo+Lqi61Q0VQ1xRnoI4hDoydxsGsEUEUsa/ADkqBblAd60bdIe0g8SiBrQNtKGlocFC70lUNVBd3wgdrLugwUwnVPbh9nsJgHOBm5/TZ4/kJ0yFQjSSi5MC/IleAvQN9vDcvwhPs1I4hiv4noRsTLoVwMam7DDsxaGAsZjrEkWt8rFDh4DxTD3MOuPBlOuZBWgY5u0NM1NTSDQ1oaEwSpJpilGvdh2MGzMGd+/eVbT+9xwEMC3lQipIcyWT5soS38oMmDIhMAdkhMTWUpZ0mVvMTqbHwnL/T5xFb2FSYaADKgQ6w2BtH2jZNIVWDUNo1TGBZgtTaPSuD/X+llAd0xaq07ug6m+9UdnXSSHtyVFukz0GHiCJhuTjD6rlVLzHhJtbUWEzqXY/IxEXsNB/ArgBclQIckSVbY6oFOaIStuc0GTnOByKv8kyUkc+fIDt27Zh+uQpsHOwQ7XWDVClZXWodKmNqnYNUdWrJdSmd4fGEjuor7SH2loHVN7ihHIU3KIogEp8CnaGwRp3GDo1h5EhF1C+mVk9yK1t0L+vB3p17wGzajWgqaGO8mYSVBnRHJW2OiuiUpVQza0AU8YPopNeYnxs0TRgNM68uMfxXaENYNGwXp6HwbZBhb9A5fKc9n+5sI3VNnki7NHH2W6o87OnT6Nrx04siw3liW3TshX6uLmhj6srWllYQlcqY9esevfG1WtXOXr+JX8FMBXAVJBMS/kMlCUw/WjdUkgUz5CJQRfXcPlDaa+yMLAItINKkCtMF7pCu2ktmOuawFTPGLpSLYg1NaAhE0PDQAaxqS7UGhuhilMjVJ7Xk6mOO+78EWcTlSx533NbtVtfXuQseSm8oUKt+gk45i7ohaie87Yh1WSQHJUCnaD9uwP6rP2JRWDyHjgIrS2aw8TAAJSiTFNNDTKpDDKZDFKxBGKxJtR1xNAw04PYogZkbcyh1tEUFUZaoKqf08cWv3nHpO/Ev0BHyLb0QfVBHaFrpAcTqQ48nFxx9NBfSHqVyCL+vHzxAju2bUevLt2gI5WhagMdVJraAZWDnDl1a359f7ZOKZIV2/e1ge5Gd6y9sT83GhOvFLic9QztD0zhAmcQrwr73dm4BKbcfrbqeicsOB/y0eP05PFjONjYMqm7npk5Fsydx4Lsk8o3LZXist/Cz1OnwbRGDairqcHDwwMJCYqtho96KuCLQpr+5Gpx6z/poGgVApiWciEtSxKWMJeSSdllFkxzAAolsCzmMMRbyYKX9swK8e1kko4dNAPdYT7ZDrrmxqirXx1Txk3E+mWr8MPQ0XDpboMOjZqjkbEpTGS6UNOVorylATT6WmD27rWfGN5czHyO1uTjSpGQSmp0lB/AUPCGACdmASse0x7anerAvIE5DLV0mP+jrkSKurXMYNOrN6ZPnoq1v6/C8nmL8etP0/Gj92j06eWApmZ1YaKlBzNdY+jr66F8c22oLeiJ8iEEdoWoogPlUAl0h8kCJ+g3NWMpzvrInVkGl/yW7WtXrsCqTRdoaktQobcpVNfJIWLBI4rx4pCXB/RbBTigwnpbDPlrKRKzX3NDK16gEhT74yJyhdpK+6RFHIu0FvQ7bbTD4P2LkaoIRkGdr165kr0U1DA2wdrVa/KbKrP4pYTlBrp6MNTXh/+WLfm2+xYrBTAVwFSQTEv5DJQlMKWXePYir3ibD095iLq7xyqkk88sqAxM7SHd2hemw7pBy1gHdavVRPhRLltMeno6SDq5cf06Dh88iNnTZqBl/abQoUADOlqgFInTpkzGiaPhSE1KwXNkYeRNX5TfasusSD8vGX2GPmVACXJg6c4MFztC3Lw6TGX6qGNQDc0aN4G7qxsWzZ/PQiA+efyELfD84k2JuDPS0/Hs2TMcPXoEyxYvRn97F9QzqglpNR2oDG2JiiyxeiEvHUEO0PH3QM3h3aBlqIumterh8IFD/BD5Hjev3wQjA0NUqqeDyrO6oXyoaxEkxQL4wX4nUu/aoEPQD7iUEMWNSUCawwU5XfPkOLQD+nFGR/mp9RkYF9Q/ZxncMWg87iZx7lUU2N7dxYUF5B/Q3xMkjRb0iYuLg7MieL+3lxfoufk3fAQwLeVCKkhzJZPmyhLfyhqY8q4oUTmpcDuzlO2pMWmDFlBlQMp7zhZpOXT9+qKmcxtoa8vQzqIl7t5RpCbMsyKSj+Gp8BMY5TUUDWqZQ6aqDm11TTQyq4MBffth2O/TUGO5GypsdkCF7S4QBSmpKPOOXZzvRGewPb4LdID5qn4w79UC1SW66N62I4789RdSU1PzUJr/18T4BPis3QB5Vys00K8BPV0dVLCuhUp+1D9ZPOfDLza2HAa+Hqju0hoymRSOvWzwKoGLH57/SMCd67fQsokFKhuJUeH71qgQ4gpRII1RAp6Q1LzZGgY+Hth6X8n9SSGVns14DIt9PyhiMuczB+I1zYMveXlP4Otvg1o+A7A78hyb0oN799GuRStoaUqwetWqgqaZW//bgoUsrVy3rl0RHU2JSr79jwCmApgKkmkpn4GyBKZMLGVBEnIw9+FeqPhTkvAiGv0wH05nGK7pg2pdGkNLU4xBHp5IT1OoEAtYD9Nfv8aRg4cx6fvxaNO8BQy1dCFWUUUlbTVUaGaAKn2aoOrsHmxftXxgEQ188i7wyt8JBCiGsI8dRoevws9L56Kath6aN26CW7duFkAlV01Ae+XSZfisWQ9Pe1fU0asOQ5ketKsboIqlESqOao6K/o4c8NM4yuPy5yFy6G50g4FdE0ilEgxw6cuC2Bc2cFREJLq0aY8qehpsjArBBKYl4AWbux2qrLfHhGPrkPJOyZc3B3iak4FBF9awVHWFvkDxQJrfHJmq1w6S9c6YfzGUTevejTuMvwZa2gjwLzgZO8+DTevWsxCG7dq0wf379/nqb/oogGkpF9KyJGEJcymZlF1mUrDRUqXIaUpp0Mx2jGASDPlQ5gsKPDjQkS3SDqgc5AaT+U4wbVwbZmI99LVywJ6QHSx59POnz/CagJWshPP5UBQcWjh9Nm3CkMGD0aapJUx1DKGnr48KlnqoOrUds4At8v6dMn3K5woLVougMbia8gjXLlxC47p1oSuTsBSHyqRR1B5Kgn3qr3CsWrAUw9wHoGOTlqipawiZVAqJqT4kXepCPLQtNBZYoQoZIBXGLwKaQHtU8+kPiwE9oKMlg1XnHnj2lM84pTz6h/OrFy6hWf2GqGwiRcUfWqMC+bsWdR+Tn7viNxJtskG37T/hXvITbgBFTlXyLl3+6DDUA9wh2pLnBYru5efFH/l++SNfT8dAOSqut8fAg4uRifeIjXyEzm3bQUtdA0sX//ZhYgWczfl5Jtu7turZC48fKyJxFdD2W6kWwFQAU0EyLeUzUKbAFMDdrFfofXxurk8n55P4GSmILaD2zJLXaJYNDOqYwFxmgNq6JqhtUJ3tjcp7WGOM13D8OmsOfDZsZEmgT586hevXriLiwQO8fPYcaYnJeJ2UgpdPn+P4kaOY9v0kWJjWg6qRGFW9LFAx0FXhk5oPwPOLOb+453dUsmDdcHM/W4eTE17B3sYamqoqGDVsGF4npyI6Igp7duzCzP9NhVMPKzSrVQ/VZXrQ05RCS1cGjbqGULVqANWJnaCxRg71IFdUDnFGeVJFF0QHqVcD7VBukx0GhC/B2lBf1DKuhppGJggICMjFBH7fWvmVY/ncRdDX0UXlxvqoOq8nyoeSircQI6dP5q6wtN1iDVMfT2yPPMWNR4Mo1LuHXz9Ag91jFMZe+fD3kz4LaUN89rFDu+AJiE6LQ9brDHh7DoR65aqwt7XD8xfPc+eb9yQqMhI9O3eBZhUVjB8zlqWhy9vmW/wugGkpF1JBmiuZNFeW+EZJDTIzyaBCefn7Fv/dP0OTQioNeHIOYkoMTnlEA8jB34nLY1oQSPCLbIADKpKbyVpnaHu1gbRLXUgtTSExN4KWoQ50pVLoaoihrSGGllQCI0MD1K1dGy2bNUO39h3g2MMKHjZOGCR3x1A3T4zzGoGRbgPRrlYjSIy1oebdCpWCPgOmhdHIpEI7VNhgB+8jy5H0ljNsob3bmdOmQV8iQZsmzTBmkDfsOvZAAyNTGFEmFYkUEhMdqDYxgmqvOtD0bgXprN6QrHeFSlAfiCjHabAiwlBBAEd0EZj6WaPx1hE4nngfCQkJcLSyhqaKCrp07Ihz58/n+wPt270Xbes3g6aOGBXkdVh8XFFwIUDG/x7KRxY1yQ5VNsgx9bQf0t8rcqgqgDTmfSqcTy1WpNrL34e1wEARSjzPbUN1W+xQ3ccTh6KvsHmFbg2Esb4BdLW1MX3qtHyNkOLj4zFh7Pcga2qzGjXwx549+fLkW6wUwFQAU0EyLeUzQEkF/vVgqpBQaAdtzaNwqAe4cK4wLFoOFzWnQIlLedEmd4tAJ6j7u0G21gV6i+UwmGYNgxGdoOfeAro9GqBm87po3qwZLM0boo5BDSbxGYg50BJLxdCUaEAs1oBErAl9qRaq6eihSi0JVL5vg0pkeMOiJeUDJrSAKy3sn6imA+xRbpMNugf9iFuvYrn1WBFz+MCuvahvYorqMl2WMFyiJYFGTR1otqwJiUNTSMd0gmSeNdQ3OKNyoCsqhDqzcIksiwxJYYWNS/xh4GIP7fWuWHNlLy8MYv/2MDSpaQ4jNSm6t+6ATavW4ta1G4iMiMTVc5ew/JeFaF+/GfTIz9TCAJV/7YaKtF/6ufE++k24PeJK6+3gEjabSYps8oSn74F05ODXiN1Q9XfmXqCU71Wcc9oJjufK54wOBS3l+ahK/HwD7aG23glLLoSx+aYlpWDoEG+INTRgbGCIYUOG4OCfB/DgwQPcv3cfe/fsgadHPxho60JLLAHlg/43hRYUwLSUC2lZkrCEuZRQyk5JRFbmv8N8v8A3egLTHID2zTbEnIBmABkeUeg+BRAUc/EuFyRHhWBHVA5xgkqIMzSCXaAR4ALpaicMDfgVV65fxY0zl3H8j8PYExKGlb7r0WlSX4jdLSF2t4DUpRmk8ibQsmkESY96qNynAVSX2eA7ClhQHFp4YCDjqM32MPMbjF0R+UTmuXcddTs3RxUzXUja14GmhyXU/tcJKstsoLbZGarBLmwuFLWoXHENf4jeAHuobJBj9IFlSEOMQ+IAAAhRSURBVH7LJQJnv8V7YP26tTA1N4W+hhRm2kZo08AC3Vq1R4s6jVBTqgddmRRVLA1RdXJHVA4iq+Z8XiT4eeZ3pPH97NHMfwROP771ySPwR9It1AgbpkhvV7BUykudBYHpR/VEBxl5bbCH174liH/LxduNjIhAPw8PJu1TQAyKgdy6eQu0tmyOGkbGLKeptkwGCjf49IliT/cTir9CxRdQKglgKoCpIJmW8hkoE2peJTDdGHMSmmSEstWGM6ZhYKAEqoV9p0WUrrP9Qc7YRkSB1IPJoMUOWsH98Vv0AUTkJCMKSYhBCqKRjh2vb6HlHxOh4esE6WY3yPxcoeXjBp2NbtBa7woNP1dUov3Sz43Nj/9JO3tU9HGA05H5OPk6Gncz4nAz4xluZz7DifQoDD2zFrrz7KA6qztkv8sh3ewOlWB3lAtxAVOp0hz4IO9833mBi6/nj8q0bLFB9cCB8I0Jx7038bid8Qy3Mp7hZtZL+MQeQ92pcog71YaWuRHL7yrRkUFspA21hgao6lAX6nN7oCpTcRN/FXul/DgFHfnxtzqgyiYnDDq7Cpczn+BOxgvcznjOjkdeP4BV+ByFG0ye6FbK/eadK993YW0C7ZlVcPvA8biRotAEAMxHd+H8+ejUrh1qGpswlS6FEaxpUg1dO3XGsqXLkBCf8BUQ8+t2KYBpKRdSQZoroTRXhvjOGSCR+8cXeL39uv/vBfeuDKaxCjD1t+H2SvNbSD9bR3utXOECrMtRzt8R4oC+sAgbj/Y7JqHt9h/QfvsPaBX2A0y2DeWAmzKyhNijXLA9yoXIUS7EkaVmo3RhvE8lLyF9UOPyAdwV6ugCaKPYuXVChqFdGI09CW22/4A2OyagXtgoVA5wZSnJvtvmxKlvg+Uol6u+/XQuH8YumpRYzt8BWgF90TpsAtrtmMjGbr3jB1iGTYDxtiHMglZ3tRsMp9lAb0Qn6AxpD+0xnSH7pTfUfJxQIYQLU8hJf8X3LVXZ7IL620ai/c4f0W77RLSlsmMiau8YgUpbnCDy5yRSFqy+AP4Vd87MtYb2TTf1x+7oj7UBZNBNUurunTuxbvUarF+zBnt378Gj6Ed5ntF/z/+UAKZlaFEXgP0fAvYypOal7DAbYk9AM9ANoq1kgFREibQI7chlpBxJVZQPlOLsbrGCaHNvrlAdXWP90JE/L8r4BLRk3aoIYlAALWxsUl3zY9P4dE40UJSlYo1ZFLo+tOHmbs/NmR+fxqVzf2uUC7RHpRAnqIa6QD3EBerBLlALdWaB+L8LJgmfpFEHLtVbAfMr7LcqRyH+yKCMzZn4rpg7fc9N9F4cnn+YW4HjKoyeqm6UY/754BK8ahKQKiyk8kDsF/n6hXFaAFMBTAU1b2mfgTIGputij0Od9uYCrcBUtIF2zKWD3DpKXShnabAtRME2iiOd20IURCEDqX9encof+brCxubb8scC2tLYNE6+4yvfQ/1waulSz1eZZ7lzz0MD0fQRbXmv87RxeUdLRFO+/fO8p/4/wzvleRT1nB/T1xqefy5A4jvS3vy7FTiFgbgApqVdSIX7//NgTGreN/92AyTFIkcGSOujj6GqL/kJduf20nytvvzRx5pzw/D5Cn1/jl42tjU3p39kfCtu7hS8v9Dx6bqCzs/NqajXaTx+TP5Y1HuL1Y7otuaMmtb3QJvNo3AtXhED+AtLhIUB3N95TQBTAQz/82BYWvV4mTBAUoApeYocibsDj2NLYBs+Fy7HFrLifGwh/qnicnwhSlUKoJ31WcC1f2quZWLc8IVwDl8E52OLID++APZH52P84dW4/lIBpn8nwv2NYwlgKoCpAKalfQZSXiGrLARtUCQHf/0uGy8zX+NZZhqeZ6YLReBByZ6BjHQ8z3jN7k3IeI2MrCy8e/f+GynvQBmAuKJME1dXEgwWwLS0C6lw/38ejDk1L+0HlQ391Zv0TMRFP8XLSCrPERf5QlHoXCgCD4r6DLxEXCSVF3gW9RQxUTGIjHr0DZXoT2iJiIjC48dPkP1WESGqGKgqgKkAhv95MCy1mjclEZllYc9UsXAkJ6fg+vWbuHr1Oq5du4Fr127i2rVbQhF4UMxn4DauXaNyC1ev3cCV699+uXz1Ku7du4esN4psOgKY/kMuEgIw/yeBuUylYAOQnZ0NAtTkpGQkJycjMTlFKAIPSvkM0HP07ZekpCSWz/b9O0WcSQFMBTAtrbQl3F/0Z6isgWkx1g+hqcABgQMKDghqXkGa/E9Kk1/yZUEAU2E9FTggcEAAUwFMBTAt5TNQ1vKZCsuiwAGBA8XngACmpVxIv6SEI/RVdNXqt8QrQTIt/sIj3CFwoKxxQABTAUwFybSUz4AgmZa1ZVGYj8CB4nNAANNSLqTfkoQk0PIPSbZlITZv8dcO4Q6BAwIHlDgggKkApoJkWspnoMzE5lVaGIRTgQMCB4rHAQFMS7mQCtLgPyQNfkO/m6DmLd6iI7QWOFAWOSCA6Te0KAvA/C8FZkHNWxbXRmFOAgeKxQEBTAUwFdS8pXwGBMm0WGuO0FjgQJnkgACmpVxIBWnyXypNfsHfXQDTMrk2CpMSOFAsDghg+gUXVQFY/6PAWlaSgxdr6RAaCxwQOKDMAQFMBTAV1LylfQbKUD5T5cVBOBc4IHCg6BwQwLS0C6lwvwDGDEzLTj7Toi8fQkuBAwIHeA4IYCqAoQCGpX0GBGtefj0RjgIH/rMcEMC0tAupcL8AxoKa9z+7gAoTFzjAc0AAUwEMBTAs7TOQq+bl/62Eo8ABgQP/NQ4IYFrahVS4XwBjwZr3v7ZuCvMVOPAJBwQwFcBQAMNSPgOCn+kn64pQIXDgP8eB/wOpGHrrEJZpQAAAAABJRU5ErkJggg==" style="text-align: left;" title="" /></div>
<div class="MsoListParagraphCxSpLast" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: 14.0pt; line-height: 107%; mso-no-proof: yes;"><!--[if gte vml 1]><v:shape
id="Picture_x0020_6" o:spid="_x0000_i1025" type="#_x0000_t75" style='width:350.4pt;
height:177pt;visibility:visible;mso-wrap-style:square'>
<v:imagedata src="file:///C:\Users\KARTIK~1\AppData\Local\Temp\msohtmlclip1\01\clip_image011.png"
o:title=""/>
</v:shape><![endif]--><!--[if !vml]--><!--[endif]--></span><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif;"><span style="font-size: x-small;">(<b>Image Credits:</b> <a href="https://images-na.ssl-images-amazon.com/images/I/C1vw862Es+S.mp4" target="_blank">Amazon</a>)</span><span style="font-size: 14pt;"><o:p></o:p></span></span></span></div>
<div class="MsoListParagraph" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;">With these five easy steps you can start shopping with Amazon
Dash Cart.<o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;"><br /></span></span></div>
<h3 style="text-align: justify;">
<b style="mso-bidi-font-weight: normal;"><u><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;">Features of Dash
Cart:</span></span></u></b></h3>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">One of the most essential feature of this smart grocery cart
is that it just not only identifies the product added to the cart, but it also
recognizes the quantity of that particular item added. Another feature include
a screen at the top where you can access your Alexa Shopping List to check
items off and view your subtotal. Moreover, another awesome feature of this cart
is that it is every cart is equipped with a coupon scanner where you can
quickly apply store coupons as you shop.</span> These features and futuristic
technology by Amazon is definitely going to save a lot of time and efforts of
users as compared to traditional way of grocery shopping.<o:p></o:p></span></div>
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<b><u><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;"><br /></span></span></u></b></div>
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<b><u><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;">Downsides of Dash Cart:</span></span></u></b></div>
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<b><u><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;"><br /></span></span></u></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Firstly, Dash Cart can only be used for small or medium sized
grocery trips, for full cart shopping customers will still have to rely on the
standard carts. Another major drawback of this cart is privacy issue of the data
of users collected because of all the tracking and surveillance</span> of
products. So, these are the possible flaws of Amazon Dash Cart that company
still may have to overcome.<o:p></o:p></span></div>
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<span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;">With this upgrade to traditional carts Amazon is trying to
bridge the gap between online and physical world shopping by making the offline
shopping quick and more convenient. With all this advance technology there is a
good chance that this cart might work well with the customers!<o:p></o:p></span></span></div>
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<span style="background: white; font-size: 14.0pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span style="font-family: "georgia" , "times new roman" , serif;"><br /></span></span></div>
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<span style="background: white; line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">Bibliography:<o:p></o:p></span></span></div>
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<span style="line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;"><a href="https://www.amazon.com/b?ie=UTF8&node=21289116011" target="_blank">https://www.amazon.com/b?ie=UTF8&node=21289116011</a><o:p></o:p></span></span></div>
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<span style="line-height: 107%;"><span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;"><a href="https://techcrunch.com/2020/07/14/amazon-to-test-dash-cart-a-smart-grocery-shopping-cart-that-sees-what-you-buy/" target="_blank">https://techcrunch.com/2020/07/14/amazon-to-test-dash-cart-a-smart-grocery-shopping-cart-that-sees-what-you-buy/</a>
<o:p></o:p></span></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;"><span style="line-height: 107%;"><a href="https://www.theverge.com/2020/7/14/21323421/amazon-dash-cart-smart-grocery-shopping-woodland-hills-store-cashierless" target="_blank">https://www.theverge.com/2020/7/14/21323421/amazon-dash-cart-smart-grocery-shopping-woodland-hills-store-cashierless</a></span><span style="background: white; line-height: 107%;"><o:p></o:p></span></span></div>
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Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com6tag:blogger.com,1999:blog-8707052728183762633.post-83895545188342793212020-07-14T08:54:00.000-07:002020-08-25T00:26:35.530-07:00The Future Of Facial Recognition<div dir="ltr" style="text-align: left;" trbidi="on">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody>
<tr><td style="text-align: center;"><img alt="Facial Recognition Concept - Facial structure algorithm " src="https://p0.pikrepo.com/preview/273/93/facial-recognition-concept-facial-structure-algorithm.jpg" style="font-family: georgia, "times new roman", serif; margin-left: auto; margin-right: auto; text-align: justify;" title="" /></td></tr>
<tr><td class="tr-caption" style="text-align: left;"><span style="font-family: georgia, "times new roman", serif; font-size: x-small; text-align: justify;">(</span><b style="font-family: georgia, "times new roman", serif; font-size: small; text-align: justify;">Image Source</b><span style="font-family: georgia, "times new roman", serif; font-size: x-small; text-align: justify;">: </span><a href="https://www.pikrepo.com/fyhko/facial-recognition-concept-facial-structure-algorithm" style="font-family: georgia, "times new roman", serif; font-size: small; text-align: left;" target="_blank">https://www.pikrepo.com/fyhko/facial-recognition-concept-facial-structure-algorithm</a><span style="font-family: georgia, "times new roman", serif; font-size: x-small; text-align: justify;">)</span></td></tr>
</tbody></table>
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<span style="font-family: "georgia" , "times new roman" , serif;"><br />The beginning of the future is here, although forced upon us
to race with time and make peace with a world that will wear masks, scrutinize
every single public interaction and create a unified population with a single
goal of making our planet virus free.<o:p></o:p></span></div>
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<br /></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">A question that keeps pondering over in my head during the
un-countable days of being locked up in my home:<o:p></o:p></span></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">How will security camera recognize faces, when I myself
fail to recognize people under masks?<o:p></o:p></span></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">I finally have stumbled upon an amazing answer, or say
technology instead; periOcular technology<o:p></o:p></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">Believe it or not Artificial Intelligence has helped un-mask
people.<o:p></o:p></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">Active match is an artificial intelligence driven solution
developed by pdActive that can be applied to existing security systems and
provide facial recognition, behavioral analysis insights under the present
conditions. The engineering teams at pdActive have used deep learning, big
data, AI and other advanced technology to reach where they are today.<o:p></o:p></span></div>
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<br /></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">What is the problem faced?<o:p></o:p></span></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">It is very difficult to identify humans when 60% of the face
is covered with masks. Combined with different viewing angles, accessories such
as hat and faces without mask it can seem virtually impossible to identify
faces.<o:p></o:p></span></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">How has this been solved?<o:p></o:p></span></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">pdActive’s periOcular technology quickly matches faces along
with millions of subjects.<o:p></o:p></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif;"><o:p> </o:p><img alt="Periocular region , Mask face in coronavirus" height="266" src="https://cdn.pixabay.com/photo/2020/03/16/16/30/coronavirus-4937561_960_720.jpg" style="text-align: left;" title="" width="400" /> </span></div>
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<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;">(<b>Image Source</b>: <a href="https://pixabay.com/photos/coronavirus-corona-virus-mask-4937561/" style="text-align: left;">https://pixabay.com/photos/coronavirus-corona-virus-mask-4937561/</a>)</span></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">What is the periocular region and how does it help?<o:p></o:p></span></b></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">Periocular technology has been an emerging trend for facial
recognition where there is no possible way to have full face or iris
recognition. The area around the human eye is called the periocular region and
has highly differentiating properties in different humans (like the stripes of
a zebra, spots on a cheetah). It is a relatively permanent trait and is highly
recognizable from distances.<o:p></o:p></span></div>
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<i><span style="font-family: "georgia" , "times new roman" , serif;">Feel free to share your thoughts , queries in the comments.</span></i></div>
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<br /></div>
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<span style="font-family: "georgia" , "times new roman" , serif;">You can find out more about advanced recognition
technologies at <a href="https://pdactive.com/" target="_blank">pdActive</a></span></div>
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<br /></div>
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<b><span style="font-family: "georgia" , "times new roman" , serif;">Bibliography:</span></b></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;"><span style="color: blue;"><a href="https://www.businesswire.com/news/home/20200713005643/en/" target="_blank">https://www.businesswire.com/news/home/20200713005643/en/</a></span></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;"><span style="color: blue;"><a href="https://pdactive.com/" target="_blank">https://pdactive.com/</a></span></span></div>
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<span style="font-family: "georgia" , "times new roman" , serif; font-size: x-small;"><a href="https://ieeexplore.ieee.org/document/6607908" target="_blank"><span style="color: blue;">https://ieeexplore.ieee.org/document/6607908</span></a><b><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p></o:p></span></b></span></div>
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Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com2tag:blogger.com,1999:blog-8707052728183762633.post-51443027893108569792020-07-12T23:03:00.001-07:002020-07-20T09:00:31.664-07:00Future of YouTube Comments Powered By Artificial Intelligence<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="letter-spacing: -0.1pt; line-height: 107%;"><span style="font-family: Georgia, Times New Roman, serif;">Google introduces its SmartReply feature for YouTube
creators. This technology with the help of artificial intelligence analyses the
message from the user and predicts the response for it.<o:p></o:p></span></span></div>
</div>
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<span style="letter-spacing: -0.1pt; line-height: 107%;"><span style="font-family: Georgia, Times New Roman, serif;">Initially, this A.I. based technology was used in Gmail,
android messages and to other android devices to suggest messages and replies.<o:p></o:p></span></span></div>
</div>
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<span style="letter-spacing: -0.1pt; line-height: 107%;"><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><span style="letter-spacing: -0.1pt; line-height: 107%;">This feature is now implemented in YouTube studio.</span><span style="letter-spacing: -0.1pt; line-height: 107%;"> From YouTube Studio’s
comments section, creators can filter, view and respond to comments from across
their channels.</span><span style="letter-spacing: -0.1pt; line-height: 107%;"> Currently, SmartReply
for YouTube is only available for English and Spanish language creators.<o:p></o:p></span></span></div>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-family: Georgia, Times New Roman, serif; letter-spacing: -0.1pt; line-height: 107%;"><br /></span></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><img alt="YouTube Comments Powered By Artificial Intelligence" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEht9PqQzqm450yRuwVhOUhgd6bK4JpQn0v0pepVxjd-UJ8WxKH0o99kofeuAHssXoKmqjAbPeEGws6aAdrTzSHf1QXDRA6If-5KJCx4gCcKt6B3BEiF5gjpMIop5BWVNrt82tFvGfbBkmti/s640/image2.gif" title="" width="640" /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif; font-size: x-small;"><b>(Image source: </b>Google)</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="letter-spacing: -0.1pt; line-height: 107%;"><span style="font-family: Georgia, Times New Roman, serif;">As YouTube developers receive enormous volume of responses
to their videos from viewers, SmartReply will save a great amount of time and
efforts in replying to each comment manually and allowing them to be in touch
with the viewers more easily. However, the YouTube replies will be still chosen
by the creator from the suggestions provided by the software. This technology will
not post anything by itself but will only provide suggestions.<o:p></o:p></span></span></div>
</div>
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<span style="font-family: Georgia, Times New Roman, serif;"><span style="letter-spacing: -0.1pt;">According to Google,”</span> <i>In comparison to emails, which
tend to be long and dominated by formal language, YouTube comments reveal
complex patterns of language switching, abbreviated words, slang, inconsistent
usage of punctuation, and heavy utilization of emoji</i>.”<span style="letter-spacing: -.1pt;"> This was the difficulty faced by Google
while implementing the SmartReply feature on YouTube.<o:p></o:p></span></span></div>
</div>
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<div style="text-align: justify;">
<span style="font-family: Georgia, Times New Roman, serif;"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiUeGfRaCDx3TEHCWGa7qjX2DF3We0vO3WZjfNp3EsIlJkVNVzDUZltkho6aV1vRC0-M2ZBrkUqRRMpqlG7PbE0ScXjtn9K3e92sWcFtNrc2rWabvZIEF4KSSn-iooF8rOqpTHYkod3t4P/s640/Emoji.png" /><b style="font-size: small;">(Image source: </b><span style="font-size: x-small;">Google)</span></span></div>
</div>
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<div style="text-align: justify;">
<span style="font-family: Georgia, Times New Roman, serif;"><span style="letter-spacing: -0.1pt;">Initially, SmartReply </span>encoded word-by-word with a neural
network, and then decoded prospective replies with another neural network.
But this method was computationally expensive, so in place of this approach, a
system was developed where it searches through a predefined list of suggestions
for the most perfect replies. This system worked well for Gmail and
other SmartReply systems but it faced challenges for YouTube (because of its
content like slang, abbreviated words, emoji, ASCII, typos, etc.), to
accomplish this Google applied a deep transform network which is able to model
words and phrases from the sequence of characters or bytes <span style="letter-spacing: -0.1pt;">and making SmartReply’s first
cross-lingual and character byte-based version of the technology</span>.<o:p></o:p></span></div>
</div>
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<div style="text-align: justify;">
<span style="font-family: Georgia, Times New Roman, serif;"><span style="letter-spacing: -0.1pt;">Ideally, they want that the software to
provide suggestions only when the creator would reply to the comment </span>and
when the model has a high chance of providing a sensible and specific response
or it is very likely to be useful to the viewer’s comment.<span style="letter-spacing: -.1pt;"><o:p></o:p></span></span></div>
</div>
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<div style="text-align: justify;">
<b><span style="font-family: Georgia, Times New Roman, serif; font-size: x-small;">Bibliography:</span></b></div>
</div>
<div style="margin-bottom: 11.25pt; margin-left: 0cm; margin-right: 0cm; margin-top: 11.25pt;">
<div style="text-align: justify;">
<a href="https://ai.googleblog.com/2020/07/smartreply-for-youtube-creators.html" target="_blank"><span style="font-family: Georgia, Times New Roman, serif; font-size: x-small;">https://ai.googleblog.com/2020/07/smartreply-for-youtube-creators.html</span></a></div>
</div>
<div style="margin-bottom: 11.25pt; margin-left: 0cm; margin-right: 0cm; margin-top: 11.25pt;">
<div style="text-align: justify;">
<a href="https://techcrunch.com/2020/07/01/google-brings-its-ai-powered-smartreply-feature-to-youtube/" target="_blank"><span style="font-family: Georgia, Times New Roman, serif; font-size: x-small;">https://techcrunch.com/2020/07/01/google-brings-its-ai-powered-smartreply-feature-to-youtube/</span></a></div>
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Kartik & Shantnuhttp://www.blogger.com/profile/18221619502768908933noreply@blogger.com10