Overview

Volume I of Mastery of Generative AI develops the conceptual foundations of large language models. From the mathematics of attention through the architectures, scaling laws, fine-tuning, alignment, and efficiency techniques that produced the systems we now call generative AI. For working engineers, ML researchers, and technical leaders who want one coherent reference for the conceptual core. Inside Volume I (Chapters 1-9): - The history of AI: from rule-based systems to deep learning - Statistical learning, neural networks, and backpropagation - Transformers from first principles - attention, normalization, positional encoding - Multimodal models: vision-language, audio, and unified embeddings - Scaling laws and the Chinchilla bargain - Fine-tuning and parameter-efficient adaptation (LoRA, adapters) - Alignment: SFT, RLHF, DPO, constitutional AI - The drive to efficiency: quantization, distillation, pruning Aligned with the NVIDIA-Certified Professional: Generative AI LLMs and NVIDIA-Certified Professional: Agentic AI exams. While this book is not an official NVIDIA publication, it covers the public technical foundations these certifications assess. 9 chapters, plus Mathematical Foundations (Appendix A), Bibliography (Appendix D), Glossary (Appendix E), and Volume I Index (Appendix F). Volume II - The Practitioner's Depth - covers distributed training, inference optimization, RAG, deployment, evaluation, and edge computing (Chapters 10-21).

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