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Key benefits

Understand the common architecture of different types of GANs

Train, optimize, and deploy GAN applications using TensorFlow and Keras

Build generative models with real-world data sets, including 2D and 3D data

Description

Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.

Who is this book for?

This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.

What you will learn

Structure a GAN architecture in pseudocode

Understand the common architecture for each of the GAN models you will build

Implement different GAN architectures in TensorFlow and Keras

Use different datasets to enable neural network functionality in GAN models

Combine different GAN models and learn how to fine-tune them

Produce a model that can take 2D images and produce 3D models

Develop a GAN to do style transfer with Pix2Pix

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

S$66.99

Generative Adversarial Networks Cookbook

S$66.99

Generative Adversarial Networks Projects

S$66.99

Total S$ 200.97

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joe m. Jun 04, 2019

I'm a full time software developer who is looking to build some GANs to learn and this book was exactly what I was looking for. Breaking down the code and referencing the original papers is perfect, especially since I do not know python extremely well.From here I plan on buying a couple other ML books and then I would like to learn more about the math details but this book got me started on the right foot for me to go find out answers to all/most of my questions.

Amazon Verified review

Oliver Wesche Apr 16, 2021

This book was very insightful and I am glad that there are such niece books available to learn about emerging ML and AI technologies.

Tony ZHu Mar 02, 2022

I have nothing to say, it does not worth your money

Y. Cheng Oct 03, 2019

A lot of none sense printed in big fonts "How to do it" "Get ready". Legend are all in very tiny small font. He likes to say "here is how to do it" Then show part of codes. Then say "now you know how to do it." He also likes to waste readers' time by saying "Do you think I will stop here? No...." This book is watery. Not substantial. Figures are crapy. Some figures are corrupted and hard to read. Some scatter plots ought to be printed with different markers, o, x , square, because it's a black and white book. But author just print three groups of markers all in circle but with different color. Which makes readingthe figure impossible from the printed book. You can go to github to download figures. But it's so stupid. The listings of the codes wasted a lot of space on import packages and set up shells. The key part of the code are often omitted. Some genereated figures by GAN are shown as a block of grey image. Author does not even bother to generate a figure to be used in his book. But author spend time to mention he owned 36 cars. Anyway, I found Another book by Rowel Atienza is much more readable and helpful. I regret so much buying this book. Don't buy it.

Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras

What do you get with eBook?