A Primer on Generative Adversarial Networks

Sanaa Kaddoura, Zayed University

Document Type

Book

Source of Publication

SpringerBriefs in Computer Science

Publication Date

1-1-2023

Abstract

This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics. The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.

ISBN

978-3-031-32660-8, 978-3-031-32661-5

ISSN

2191-5776

Publisher

Springer International Publishing

Disciplines

Computer Sciences

Keywords

GANs, Generative Adversarial Networks

Indexed in Scopus

no

Open Access

no

Link to Full Text

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