A classic problem used to benchmark algorithms in machine learning and computer vision is the task of classifying which handwritten digit from 0-9 is represented in a pixelated image from the MNIST dataset. A large breakthrough on this problem occured in 2006, when researches at the University of Toronto and the National University of Singapore discovered a way to train deep neural networks to perform this task.
One of their ciritical observaltions was that instead of training a network to directly predict the most likely digit(Y) given an image(X), it was more effective to first train a network that could generate images, and then classify them as a secound step.
In Chapter 4, Teaching Networks to Generate Digits, I will describe how this model improved upon past attempts, and how to create your own restricted Boltzmann machine and deep Boltzmann machine models that can generate new MNIST digit images.
Generating images
A challenge to generating images such as the Portraint of Edmond Belamy with the approach used for the MNIST dataset is that frequently, images have no labels (such as a digit); rather, we want to map
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