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Full Description
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.
Contents
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