Overview

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: learning 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 All code is available publicly as open source on github.

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