Almost every book about DL uses the MNIST dataset to show you the power of DL, which, over the years, has made this dataset extremely boring, like a fruit fly for genetic researchers. To break this tradition, and add a bit more fun to the book, I've tried to avoid well-beaten paths and illustrate PyTorch using something different. You may have heard about generative adversarial networks (GANs), which were invented and popularized by Ian Goodfellow. In this example, we'll train a GAN to generate screenshots of various Atari games.

The simplest GAN architecture is this: we have two networks and the first works as a "cheater" (it is also called generator), and the other is a "detective" (another name is discriminator). Both networks compete with each other: the generator tries to generate fake data, which will be hard for the discriminator to distinguish from your dataset, and the discriminator tries to detect the generated data samples. Over time, both networks...