Springer Generative AI: Foundations & Concepts

This textbook covers the foundations, concepts, and implementations of Generative AI. It offers students with the knowledge and skills essential for the evolving field of Generative AI. It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. The implementation of each generative AI model is also part of the textbook. The textbook consists of four parts. Part 1 which consists of two chapters is an introduction to Generative AI. It provides an exploration of the fundamental concepts underlying the field of generative Artificial Intelligence. It builds a strong foundation for the reader to understand the remaining parts. In the Part 2, all the core concepts of Generative AI like Variational Autoencoders, Generative Adversarial Networks, Normalizing Flow Models Autoregressive Models, Energy-based and Diffusion Models and Large Language Models (LLMs) are covered. In Part 3, applications of Generative AI including World Models, Content Generation with Generative Models and Building Applications with LLMs are discussed. Finally, Part 4, looks at the ethical considerations for the development and deployment of Generative AI models. Chapter-wise complete source codes are delivered as an additional resource. Practical exercises are also included at the end of each chapter. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The book is suitable for both undergraduate and graduate students in computer science and engineering. This textbook is a valuable resource for students who want to study the domain of Generative AI.