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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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Introduction

There are a few modern techniques for converting 2D images into 3D models and this chapter seeks to simplify those into easy-to-understand techniques. This chapter will cover recipes that encode 2D images into a latent space-like representation, 3D convolutions for learning on 3D data, and visualization of those results.

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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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