Generative AI: the Mathematics and Architecture Behind It: From of Transformer to Nested Learning All in One Volume

Beyond the Prompt: An Architect's Vision of Generative AI We live in an era where artificial intelligence has become an everyday reality, yet the complex interactions happening inside these systems remain hidden in a black box. While we are inundated with countless research papers and fragmented technical details, what we truly need is a macro-level blueprint-an understanding of why these technologies were designed the way they are, and how they function together as a unified intelligence. This book refuses to be a simple laundry list of tech trends. Instead, it anchors the core of generative AI on two main pillars: mathematical first principles and architectural design, structuring them so you can take in the entire landscape at a single glance. The 6 Pillars of Intellectual Design - Discovering the Mathematical DNA: Understand the "language" through which models perceive the world using the tools of information theory, probability, and optimization.- The Backbone of Intelligence-The Transformer: Dissect the fundamental principles behind how embeddings and attention mechanisms pinpoint the coordinates of meaning within text.- The Aesthetics of Efficiency & Optimization: Moving beyond simple quantization and PEFT, we lay out the blueprints of GPT and DeepSeek side by side to analyze their strategic mathematical trade-offs.- The Next-Gen Paradigm-Nested Learning: Look beyond static architectures to glimpse the future of "self-correcting intelligence," where the boundaries between training and inference begin to blur.- Human Alignment: Explore the mathematical mechanisms behind RLHF and reasoning algorithms that align a model's raw intelligence with human values.- Expanding to Integrated Intelligence: Witness the modern frontier of AI as it evolves beyond text and expands into true multimodality. Who is This Book For? This book is written for undergraduate and graduate students, industry researchers, and tech strategists (PMs) who already possess a foundational understanding of deep learning and are hungry for the next level of insight. Rather than getting bogged down in granular implementation code, this book serves as a robust intellectual compass for those who want to master the overarching flow of these models and grasp their fundamental design philosophies. Only by weaving fragmented pieces of knowledge into a single, grand narrative will you truly evolve from a mere user into a visionary "Architect of Models."