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.

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.

Make Your First GAN With PyTorch