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 accelerationIt 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 stabilityBeyond the very basics, readers can explore more sophisticated GANs: * convolutional GANs for generated higher quality images * conditional GANs for generated images of a desired classThe 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