Generative AI – Concepts, Tools and Applications offers a comprehensive exploration of the science and practice of machine creativity. The book begins with the evolution of AI and the mathematical principles underlying generative models, then examines key architectures such as Variational Autoencoders, Generative Adversarial Networks, diffusion models, and large language models. A dedicated section highlights popular tools and platforms—including open-source libraries and cloud-based frameworks—that enable developers to build and deploy generative AI solutions. Real-world applications are showcased across diverse sectors: content creation, healthcare diagnostics, drug discovery, design automation, gaming, and education.
The text also addresses critical ethical and regulatory considerations, covering topics like bias mitigation, intellectual property, and responsible AI deployment. Each chapter combines conceptual explanations with examples, diagrams, and practical insights to connect theory with application. This book is ideal for students of computer science, data science professionals, researchers, and industry innovators who wish to understand and apply generative AI. By merging core concepts with emerging trends, it serves as both a foundational reference and a guide to future possibilities in this dynamic field.