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Hands-On Generative Adversarial Networks with Keras

You're reading from Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789538205

Length 272 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Neural Networks

Author (1):

Rafael Valle

View More author details

Table of Contents (14) Chapters

Preface

1. Section 1: Introduction and Environment Setup

2. Deep Learning Basics and Environment Setup FREE CHAPTER

3. Introduction to Generative Models

4. Section 2: Training GANs

5. Implementing Your First GAN

6. Evaluating Your First GAN

7. Improving Your First GAN

8. Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

9. Progressive Growing of GANs

10. Generation of Discrete Sequences Using GANs

11. Text-to-Image Synthesis with GANs

12. TequilaGAN - Identifying GAN Samples

13. Whats next in GANs

Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

In section three, you will implement state-of-the-art models for computer vision, natural language processing, and audio processing using GANs.

The following chapters will be covered in this section:

Chapter 6, pix2pixHD – Synthesizing and Manipulating Images with GANs
Chapter 7, Progressive Growing of GANs
Chapter 8, Generation of Discrete Sequences Using GANs
Chapter 9, Text-To-Image Synthesis with GANs
Chapter 10, Speech Enhancement with GANs
Chapter 11, TequilaGAN: Identifying GAN Samples
Chapter 12, What’s Next in GANs

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Authors (1)

Rafael Valle

Rafael Valle

Rafael Valle is a research scientist at NVIDIA focusing on audio applications. He has years of experience developing high performance machine learning models for data/audio analysis, synthesis and machine improvisation with formal specifications. Dr. Valle was the first to generate speech samples from scratch with GANs and to show that simple yet efficient techniques can be used to identify GAN samples. He holds an Interdisciplinary PhD in Machine Listening and Improvisation from UC Berkeley, a Masters degree in Computer Music from the MH-Stuttgart in Germany and a Bachelors degree in Orchestral Conducting from UFRJ in Brazil.

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