Make Your First GAN With PyTorch

A gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch.

This beginner-friendly guide will give you hands-on experience:
* understanding PyTorch basics
* developing your first PyTorch neural network
* exploring neural network refinements to improve performance
* introduce CUDA GPU acceleration
It will introduce GANs, one of the most exciting areas of machine learning:
* introducing the concept step-by-step, in plain English
* coding the simplest GAN to develop a good workflow
* growing our confidence with an MNIST GAN
* progressing to develop a GAN to generate full-colour human faces
* experiencing how GANs fail, exploring remedies and improving GAN performance and stability
Beyond the very basics, readers can explore more sophisticated GANs:
* convolutional GANs for generated higher quality images
* conditional GANs for generated images of a desired class
The appendices will be useful for students of machine learning as they explain themes often skipped over in many courses:
* calculating ideal loss values for balanced GANs
* probability distributions and sampling them to create images
* carefully chosen examples illustrating how convolutions work
* a brief explanation of why gradient descent isn't suited to adversarial machine learning