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Generative Adversarial Networks Cookbook

You're reading from   Generative Adversarial Networks Cookbook Over 100 recipes to build generative models using Python, TensorFlow, and Keras

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789139907
Length 268 pages
Edition 1st Edition
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Author (1):
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Josh Kalin Josh Kalin
Author Profile Icon Josh Kalin
Josh Kalin
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Toc

Table of Contents (10) Chapters Close

Preface 1. What Is a Generative Adversarial Network? 2. Data First, Easy Environment, and Data Prep FREE CHAPTER 3. My First GAN in Under 100 Lines 4. Dreaming of New Outdoor Structures Using DCGAN 5. Pix2Pix Image-to-Image Translation 6. Style Transfering Your Image Using CycleGAN 7. Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN 8. From Image to 3D Models Using GANs 9. Other Books You May Enjoy

To get the most out of this book

A basic knowledge of Python is a prerequisite, while a familiarity with machine learning concepts will be helpful.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Generative-Adversarial-Networks-CookbookIn case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You can run the nvidia-smi command to know which version of driver is installed on your system."

A block of code is set as follows:

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

Any command-line input or output is written as follows:

sudo ./build.sh

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Now, click Save and let's check to make sure that we have the appropriate directory structure with our files."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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