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Hands-On Generative Adversarial Networks with Keras Cover

Hands-On Generative Adversarial Networks with Keras

Your guide to implementing next-generation generative adversarial networks

Publisher:Packt Publishing Limited
By:   
Paid access
|May 2019
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Description

Develop generative models for a variety of real-world use cases and deploy them to production

Key Features

  • 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

Book 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

Who this book is for

This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a mix of theory and hands-on content to implement GANs using Keras. Working knowledge of Python is expected.

Table of Contents

  1. Deep Learning Basics and Environment Setup
  2. Introduction to Generative Models
  3. Implementing your fist GAN
  4. Evaluating your first GAN
  5. Improving your first GAN
  6. Synthesizing and Manipulating Images with GANs
  7. Progressive Growing of GANs
  8. Natural Language Generation with GANs
  9. Text-To-Image Synthesis with GANs
  10. Speech Enhancement with GANs
  11. TequilaGAN: Identifying GAN samples
  12. What
https://github.com/packtpublishing/hands-on-generative-adversarial-networks-with-keras
PDF ISBN: 978-1-78953-513-6
Publisher: Packt Publishing Limited
Copyright owner: © 2019 Packt Publishing
Publication date: 2019
Language: English
Pages: 272
Related subjects:
Computer sciences,
Computer sciences, other
BISAC subjects:
COM037000,
COM072000,
COM016000
Thema subjects:
UYA,
UYQM,
UYM,
UYQV,
UYQP

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