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Hands-On Generative Adversarial Networks with Keras
Hands-On Generative Adversarial Networks with Keras

Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks

By Rafael Valle
$29.99 $20.98
Book May 2019 272 pages 1st Edition
eBook
$29.99 $20.98
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eBook
$29.99 $20.98
Print
$43.99
Subscription
$15.99 Monthly

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Product Details


Publication date : May 3, 2019
Length 272 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789538205
Category :
Table of content icon View table of contents Preview book icon Preview Book

Hands-On Generative Adversarial Networks with Keras

Section 1: Introduction and Environment Setup

This section features an introduction to the basics of deep learning, how to set up a deep learning environment with Python and Keras, and how to go about acquiring data. You will also benefit from an introduction to generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Normalizing Flow (NF).

The following chapters will be covered in this section:

  • Chapter 1, Deep Learning Basics and Environment Setup
  • Chapter 2, Introduction to Generative Models
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Key benefits

  • Discover various GAN architectures using a Python and Keras library
  • Understand how GAN models function with the help of theoretical and practical examples
  • Apply your learnings to become an active contributor to open source GAN applications

Description

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA

What you will learn

Discover how GANs work and the advantages and challenges of working with them Control the output of GANs with the help of conditional GANs, using embedding and space manipulation Apply GANs to computer vision, natural language processing (NLP), and audio processing Understand how to implement progressive growing of GANs Use GANs for image synthesis and speech enhancement Explore the future of GANs in visual and sonic arts Implement pix2pixHD to turn semantic label maps into photorealistic images

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : May 3, 2019
Length 272 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789538205
Category :

Table of Contents

14 Chapters
Preface Chevron down icon Chevron up icon
Section 1: Introduction and Environment Setup Chevron down icon Chevron up icon
Deep Learning Basics and Environment Setup Chevron down icon Chevron up icon
Introduction to Generative Models Chevron down icon Chevron up icon
Section 2: Training GANs Chevron down icon Chevron up icon
Implementing Your First GAN Chevron down icon Chevron up icon
Evaluating Your First GAN Chevron down icon Chevron up icon
Improving Your First GAN Chevron down icon Chevron up icon
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio Chevron down icon Chevron up icon
Progressive Growing of GANs Chevron down icon Chevron up icon
Generation of Discrete Sequences Using GANs Chevron down icon Chevron up icon
Text-to-Image Synthesis with GANs Chevron down icon Chevron up icon
TequilaGAN - Identifying GAN Samples Chevron down icon Chevron up icon
Whats next in GANs Chevron down icon Chevron up icon

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