Generative Adversarial Networks (GANs)

Adele Kuzmiakova (Edited by)

9781779564177

Hardback, published 31 March 2026

240 pages
22.9 x 15.2 x 1.5 cm, 0.666 kg

Generative Adversarial Networks (GANs) are a class of machine learning models that have transformed the fields of artificial intelligence and creative technologies. By pitting two neural networks against each other, GANs generate highly realistic data, from images to text. This book explores the architecture, training methods, and diverse applications of GANs in healthcare, media, and research. With its in-depth analysis, it is essential for students, data scientists, and AI practitioners seeking to master this groundbreaking technology.

• Chapter 1 Introduction to Generative Adversarial Networks (GANs)
• Chapter 2 Architecture of Generative Adversarial Networks
• Chapter 3 Types of Generative Adversarial Networks
• Chapter 4 Training Generative Adversarial Networks (GANs)
• Chapter 5 Security Issues in Generative Adversarial Networks
• Chapter 6 Image Editing Using GANs
• Chapter 7 Practical Applications of GANs
• Chapter 8 Advanced Concepts in GANs