The TensorFlow library and digit recognition

For the exercises in this chapter, we will be using the TensorFlow library open-sourced by Google (available at https://www.tensorflow.org/). Installation instructions vary by operating system. Additionally, for Linux systems, it is possible to leverage both the CPU and graphics processing unit (GPU) on your computer to run deep learning models. Because many of the steps in training (such as the multiplications required to update a grid of weight values) involve matrix operations, they can be readily parallelized (and thus accelerated) by using a GPU. However, the TensorFlow library will work on CPU as well, so don't worry if you don't have access to an Nvidia GPU card.

The MNIST data

The data we will be examining in this exercise is a set of images of hand-drawn numbers from 0 to 9 from the Mixed National Institute of Standards and Technology (MNIST) database (LeCun, Yann, Corinna Cortes, and Christopher JC Burges. The MNIST database of handwritten...