“Generative AI: Foundations, Models, and Applications” provides a comprehensive exploration of one of the most transformative areas of artificial intelligence. Designed for students, researchers, educators, and professionals, this book offers a balanced blend of theory, models, and real-world use cases of generative AI.
The text begins by introducing the “fundamentals of generative modeling”, covering the mathematical and conceptual underpinnings that make generative AI possible. It then progresses into a detailed study of prominent models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Large Language Models (LLMs), and Multimodal Systems.
Beyond the foundations, the book emphasizes the practical applications of generative AI across
industries—ranging from natural language processing, computer vision, and healthcare to education, creative arts, business innovation, and scientific research. Ethical considerations, challenges, and future trends are also discussed, equipping readers to critically analyze the opportunities and risks of this rapidly evolving field.
With clear explanations, case studies, and an application-driven perspective, this book serves as both an academic resource and a practical guide for those seeking to understand and leverage
generative AI in real-world contexts.
