Case study - handwritten digit classification

We will be using a multiclass classification network to recognize the corresponding class of a handwritten digit. As before, you can find the complete code for this chapter in the book's Git repository, under Chapter05, if you'd like to follow along.

Problem definition

The MNIST dataset has become an almost canonical neural network dataset. This dataset consists of images of 60,000 handwritten digits, belonging to 10 classes representing their respective digit (0,1,2...9). Because this dataset has become so common, many deep learning frameworks come with an MNIST loading method built into the API. Both TensorFlow and Keras have one, and we will be using the Keras MNIST...