“Study of Generative Artificial Intelligence with Machine Learning and Deep Learning” provides an insightful journey into the world of Generative AI, covering its theoretical foundations, practical implementations, and emerging trends. This book is structured to offer a progressive learning experience, beginning with fundamental AI concepts and advancing toward sophisticated generative techniques.

The book is divided into nine well-organised chapters. It starts with an introduction to Generative AI, explaining its principles, evolution, and ethical considerations. This is followed by a deep dive into machine learning and deep learning fundamentals, setting the groundwork for understanding generative models.

Subsequent chapters introduce readers to generative models, discussing their importance, various concepts such as latent variables and Bayesian methods, and applications in data simulation. The book further explores advanced generative AI techniques, including Variational Autoencoders, Generative Adversarial Networks, Transformers, and Diffusion Models, providing practical insights into their working mechanisms.

A dedicated section examines the role of machine learning and deep learning in enhancing generative models, discussing hybrid approaches, training challenges, and breakthroughs in AI-generated images and text. Ethical, legal, and security concerns are critically analysed, addressing issues such as AI bias, misinformation, and regulatory complexities.

Concluding with a discussion on the future of Generative AI, the book speculates on Artificial General Intelligence, autonomous AI agents, and humanoid robots, presenting a visionary outlook on AI’s limitless potential.

This book serves as a valuable reference for students, researchers, and industry professionals seeking to understand, develop, and apply Generative AI effectively.