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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?

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

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Exercise

You really didn't think I'd let you get out of this chapter without some homework, did you? I've got a few basic problems to get you ready for the following lessons:

Produce a CNN based on ImageNet to classify all of the MSCOCO classes.

Hint: There are plenty of available models out there. Simply get one running in inference mode and see how it works.

Draw the basic GAN architecture. Now, draw an improvement to that architecture based on the topics you learned in this chapter. Think of the generator and discriminator as building blocks.

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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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