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Hands-On Generative Adversarial Networks with Keras

By Rafael Valle
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  1. Free Chapter
    Section 1: Introduction and Environment Setup
About this book
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
Publication date:
May 2019
Publisher
Packt
Pages
272
ISBN
9781789538205

 

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
About the Author
  • 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.

    Browse publications by this author
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