Generative AI: Foundations and Concepts

Generative AI brings together the ideas behind today’s generative models and the ways they are built, applied, and evaluated. This textbook moves from foundational concepts into model architectures, applications, and ethical questions, giving readers a structured route through a field that spans machine learning theory and working implementations.

From core ideas to model families

The opening sections introduce generative modeling and deep learning before turning to the principal approaches in the field. Chapters examine autoencoders, generative adversarial networks, autoregressive models, normalizing flows, energy-based and diffusion models, and large language models. This progression helps place different techniques within a broader picture rather than treating each model as an isolated development.

Connect theory with implementation

Concepts are developed through explanations that move toward mathematical and algorithmic detail, alongside Python-based code examples. The book also includes exercises, summaries, examples, diagrams, and case studies—features that make it suitable for coursework as well as focused independent reference.

Applications, prompting, and responsibility

Beyond model design, the text explores content generation, world models, and prompt engineering. Its final section considers the ethical dimensions of generative AI, including concerns that arise in the development and deployment of these systems. Together, these topics extend the discussion from how models work to where they are used and what questions their use raises.

For students and working readers

Written to support both introductory and advanced study, this textbook is aimed at undergraduate and graduate students in computer science and engineering, as well as professionals and researchers looking for a reference they can consult by topic. The authors note that basic familiarity with machine learning is helpful; no advanced mathematical background is assumed.

For readers seeking a clear, organized foundation in generative AI—from its underlying models to practical applications and ethical considerations—this book offers a substantial course of study in one volume.