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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

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789139907

Length 268 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Artificial Intelligence

Author (1):

Josh Kalin

View More author details

Table of Contents (10) Chapters

Preface

1. What Is a Generative Adversarial Network? FREE CHAPTER

2. Data First, Easy Environment, and Data Prep

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

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Dreaming of New Outdoor Structures Using DCGAN

This chapter will cover the building blocks required to build your first Deep Convolutional Generative Adversarial Network (DCGAN) implementation, including the following recipes:

What is a DCGAN? A simple pseudocode example
Tools—do I need any unique tools?
Parsing the data—is our data unique?
Code implementation—generator
Code implementation—discriminator
Training
Evaluation—how do we know it worked?
Adjusting parameters for better performance

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Authors (1)

Kalin

Kalin

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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