before we beginthe mathematical building blocks of neural networks the images are encoded as numpy arraysand the labels are an array of digitsranging from to the images and labels have one-to-one correspondence let' look at the training datatrain_images shape ( len(train_labels train_labels array([ ]dtype=uint and here' the test datatest_images shape ( len(test_labels test_labels array([ ]dtype=uint the workflow will be as followsfirstwe'll feed the neural network the training datatrain_images and train_labels the network will then learn to associate images and labels finallywe'll ask the network to produce predictions for test_imagesand we'll verify whether these predictions match the labels from test_labels let' build the network--againremember that you aren' expected to understand everything about this example yet listing the net work archit ect ure from keras import models from keras import layers network models sequential(network add(layers dense( activation='relu'input_shape=( ,))network add(layers dense( activation='softmax')the core building block of neural networks is the layera data-processing module that you can think of as filter for data some data goes inand it comes out in more useful form specificallylayers extract representations out of the data fed into them--hopefullyrepresentations that are more meaningful for the problem at hand most of deep learning consists of chaining together simple layers that will implement form of progressive data distillation deep-learning model is like sieve for data processingmade of succession of increasingly refined data filters--the layers hereour network consists of sequence of two dense layerswhich are densely connected (also called fully connectedneural layers the second (and lastlayer is -way softmax layerwhich means it will return an array of probability scores (summing to each score will be the probability that the current digit image belongs to one of our digit classes licensed to