first look at neural network let' look at concrete example of neural network that uses the python library keras to learn to classify handwritten digits unless you already have experience with keras or similar librariesyou won' understand everything about this first example right away you probably haven' even installed keras yetthat' fine in the next we'll review each element in the example and explain them in detail so don' worry if some steps seem arbitrary or look like magic to youwe've got to start somewhere the problem we're trying to solve here is to classify grayscale images of handwritten digits ( pixelsinto their categories ( through we'll use the mnist dataseta classic in the machine-learning communitywhich has been around almost as long as the field itself and has been intensively studied it' set of , training imagesplus , test imagesassembled by the national institute of standards and technology (the nist in mnistin the you can think of "solvingmnist as the "hello worldof deep learning--it' what you do to verify that your algorithms are working as expected as you become machine-learning practitioneryou'll see mnist come up over and over againin scientific papersblog postsand so on you can see some mnist samples in figure note on classes and labels in machine learninga category in classification problem is called class data points are called samples the class associated with specific sample is called label figure mnist sample digit you don' need to try to reproduce this example on your machine just now if you wish toyou'll first need to set up keraswhich is covered in section the mnist dataset comes preloaded in kerasin the form of set of four numpy arrays listing loading he mnist dat aset in keras from keras datasets import mnist (train_imagestrain_labels)(test_imagestest_labelsmnist load_data(train_images and train_labels form the training setthe data that the model will learn from the model will then be tested on the test settest_images and test_labels licensed to