Generative Adversarial Networks Cookbook

This is the code repository for Generative Adversarial Networks Cookbook, published by Packt.

Over 100 recipes to build generative models using Python, TensorFlow, and Keras

What is this book about?

Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.

This book covers the following exciting features: 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

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-8).

Software and Hardware List

Chapter	Software required	OS required
2-8	nvidia-docker-2	Linux 16.04 or above
2-8	CUDA driver 387 or above	Linux 16.04 or above

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Get to Know the Author

Josh Kalin is a Physicist and Technologist focused on the intersection of robotics and machine learning. Josh works on advanced sensors, industrial robotics, machine learning, and automated vehicle research projects. Josh holds degrees in Physics, Mechanical Engineering, and Computer Science. In his free time, he enjoys working on cars (has owned 36 vehicles and counting), building computers, and learning new techniques in robotics and machine learning (like writing this book).

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