In this chapter, we offer you essential knowledge for building and training deep learning models, including Generative Adversarial Networks (GANs). We are going to explain the basics of deep learning, starting with a simple example of a learning algorithm based on linear regression. We will also provide instructions on how to set up a deep learning programming environment using Python and Keras. We will also talk about the importance of computing power in deep learning; we are going to describe guidelines to fully take advantage of NVIDIA GPUs by maximizing the memory footprint, enabling the CUDA Deep Neural Network library (cuDNN), and eventually using distributed training setups with multiple GPUs. Finally, in addition to installing the libraries that will be necessary for upcoming projects in this book, you will test your installation...
Hands-On Generative Adversarial Networks with Keras
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Hands-On Generative Adversarial Networks with Keras
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Overview of 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
Table of Contents (14 chapters)
Preface
Free Chapter
Section 1: Introduction and Environment Setup
Deep Learning Basics and Environment Setup
Introduction to Generative Models
Section 2: Training GANs
Implementing Your First GAN
Evaluating Your First GAN
Improving Your First GAN
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
Progressive Growing of GANs
Generation of Discrete Sequences Using GANs
Text-to-Image Synthesis with GANs
TequilaGAN - Identifying GAN Samples
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