Generative AI 2.0 : Beyond Text and Image

"Generative AI 2.0 : Beyond Text and Images" is a comprehensive textbook and practical guide designed to navigate the next frontier of Artificial Intelligence. It serves as both an introduction for newcomers and a deep-dive for advanced learners, bridging the gap between foundational concepts and cutting-edge applications in the rapidly evolving domain of Generative AI.


Pedagogy:

1. This book is designed in strict compliance with the pedagogical principles, emphasizing holistic and experiential learning. The pedagogical tools employed include:
2. Progressive Learning Arc: The chapters are arranged in a logical sequence, starting with fundamental concepts and progressively building up to advanced, state-of-the-art topics.
3. Real-World Case Studies: Each chapter features case studies that showcase how Generative AI models are being used by companies like Google, OpenAI, and NVIDIA to solve real-world problems.
4. Hands-On Practical Labs: Designated sections within chapters provide step-by-step guidance for implementing models, fine-tuning pre-trained architectures, and using APIs.
5. End-of-Chapter Assessments: Each chapter concludes with a set of conceptual questions, programming exercises, and research prompts to reinforce learning and encourage critical thinking.
6. Capstone Project-Based Learning: The book culminates in a large-scale project that integrates knowledge from all preceding chapters, providing a holistic and practical learning experience.


Key Features:


1. Globally Relevant Curriculum: The syllabus is designed to be compatible with leading computer science programs worldwide, ensuring students are learning globally competitive skills.
2. Beginner to Advanced Coverage: The book caters to both undergraduate students starting their AI journey and postgraduate students or professionals looking to specialize in Generative AI.
3. Focus on "GenAI 2.0": Goes beyond standard text and image generation to provide in-depth coverage of video, 3D, audio, and code generation, as well as autonomous agents.
4. Practical and Code-Centric: Rich with working code examples, practical tutorials, and a complete end-to-end project, primarily using Python and popular libraries like PyTorch, TensorFlow, and Hugging Face.
5. Ethics and Responsible AI: A dedicated chapter on the ethical implications, biases, and governance of Generative AI, a crucial topic for future engineers.


To Whom This Book is For

1. B.Tech/M.Tech Computer Science Students: The primary audience, for whom this book serves as a core or elective textbook.
2. AI and Machine Learning Practitioners: Professionals looking to upskill and transition into the specialized field of Generative AI.
3. Academic Researchers: A comprehensive reference for exploring the architectures and applications of next-generation generative models.
4. Software Developers and Engineers: Individuals interested in integrating Generative AI capabilities into their products and services.
5. Self-Taught Learners and Enthusiasts: Anyone with a passion for AI who wants a structured, practical, and comprehensive guide to the field.