9 Books on Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al.

titled “Generative Adversarial Networks.

”Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images.

As such, a number of books have been written about GANs, mostly focusing on how to develop and use the models in practice.

In this post, you will discover books written on Generative Adversarial Networks.

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Most of the books have been written and released under the Packt publishing company.

Almost all of the books suffer the same problems: that is, they are generally low quality and summarize the usage of third-party code on GitHub with little original content.

This particularly applies to the books from Packt.

Nevertheless, it is useful to have an idea of what books are available and the topics covered.

This can be helpful both in choosing a book for self-study and to get an idea of the types of topics you may want to explore when getting started with GANs.

We will review the following seven books:Additionally, we will also review the GAN section of two popular deep learning books.

If I have missed a book on GANs, please let me know in the comments below.

The books mostly seem to cover the same GAN architectures, such as:Let’s take a closer look at the topics covered by each book.

Title: GANs in Action: Deep learning with Generative Adversarial NetworksWritten by Jakub Langr and Vladimir Bok, published in 2019.

This book provides a gentle introduction to GANs using the Keras deep learning library.

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Download Your FREE Mini-CourseTitle: Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and PlayWritten by David Foster, published in 2019.

Generative Deep LearningThis book focuses on the more general problem of generative modeling with deep learning, allowing variational autoencoders to be discussed.

It does cover a range of GAN models, but also language modeling with LSTMs.

Title: Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and moreWritten by Rowel Atienza, published in 2018.

This book is on the more general topic of advanced deep learning with Keras, allowing the coverage of autoencoders, variational autoencoders, and deep reinforcement learning.

Nevertheless, the book has four chapters on GANs and I consider it a GAN book.

Advanced Deep Learning with KerasTitle: Learning Generative Adversarial Networks: Next-generation deep learning simplified.

Written by Kuntal Ganguly, published in 2017.

This book provides a very simple introduction to GANs.

The book may have been removed or unpublished by Packt and replaced with a video course.

Learning Generative Adversarial NetworksTitle: Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras.

Written by Kailash Ahirwar, published in 2019.

This book summarizes a range of GANs with code examples in Keras.

Generative Adversarial Networks ProjectsTitle: Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and KerasWritten by Josh Kalin, published in 2018.

Generative Adversarial Networks CookbookTitle: Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networksWritten by Rafael Valle, published in 2019.

This may be one of the better Packt published books as the code appears to be better quality and a wider array of GANs are covered.

Hands-On Generative Adversarial Networks with KerasThe topic of GANs has been covered in other modern books on deep learning.

Two important examples are listed below.

GANs were described in the 2016 textbook titled “Deep Learning” by Ian Goodfellow, et al.

, specifically:Section 20.


4 titled “Generative Adversarial Networks” provides a short introduction to GANs at the time of writing, two years after the original paper.

It would be great to see Goodfellow write a dedicated textbook on the topic sometime in the future.

Deep LearningGANs were also covered by Francois Chollet in his 2017 book titled “Deep Learning with Python“, specifically:In Section 8.

5 titled “Introduction to generative adversarial networks,” the topic of GANs is introduced and a worked example of developing a GAN for one image class (frogs) in the CIFAR-10 dataset is covered.

Source code is provided here:Deep Learning with PythonIn this post, you discovered a suite of books on the topic of Generative Adversarial Networks, or GANs.

Have you read any of the listed books?.Let me know what you think of it in the comments below.

Do you have any questions?.Ask your questions in the comments below and I will do my best to answer.

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with just a few lines of python codeDiscover how in my new Ebook: Generative Adversarial Networks with PythonIt provides self-study tutorials and end-to-end projects on: DCGAN, conditional GANs, image translation, Pix2Pix, CycleGAN and much more.

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