{"product_id":"modern-large-language-models-a-first-principles-guide-to-building-and-understanding-transformer-based-language-models-paperback","title":"Modern Large Language Models: A First-Principles Guide to Building and Understanding Transformer-Based Language Models - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eDaniel R. Holt\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eLarge language models now sit at the core of modern software systems. They power search, recommendation engines, coding assistants, conversational interfaces, and autonomous agents. Yet for many engineers and practitioners, these models remain opaque-understood through fragments of code, borrowed recipes, or surface-level explanations.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis book was written to change that.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eModern Large Language Models\u003c\/em\u003e is a clear, systems-level guide to understanding how transformer-based language models actually work-starting from first principles and building upward toward complete, modern LLM systems.\u003c\/p\u003e\u003cp\u003eRather than treating large language models as black boxes, this book explains the fundamental ideas that make them possible: probabilistic language modeling, vector representations, attention mechanisms, optimization, and architectural composition. Concepts are introduced gradually, with visual intuition and concrete reasoning before full implementations, allowing readers to develop understanding that transfers beyond any single framework or model version.\u003c\/p\u003e\u003cp\u003eThe book takes you from the foundations of language modeling to the realities of training, fine-tuning, evaluation, and deployment. Along the way, it connects theory to practice, showing how design decisions shape model behavior, performance, and limitations.\u003c\/p\u003e\u003cp\u003eThis is not a collection of shortcuts or prompt recipes. It is a guide for readers who want to reason about large language models as \u003cstrong\u003eengineered systems\u003c\/strong\u003e-systems that can be analyzed, debugged, improved, and deployed with confidence.\u003c\/p\u003e\u003cp\u003eWhat You'll Learn\u003c\/p\u003e\u003cp\u003e- How language modeling works at a probabilistic level-and why it matters\u003cbr\u003e- How tokens, embeddings, and vector spaces encode meaning\u003cbr\u003e- How self-attention and transformer architectures operate internally\u003cbr\u003e- How complete GPT-style models are built from first principles\u003cbr\u003e- How training pipelines work, including optimization and scaling considerations\u003cbr\u003e- How fine-tuning, instruction tuning, and preference optimization fit together\u003cbr\u003e- How embeddings, retrieval, and RAG systems extend model capabilities\u003cbr\u003e- How modern LLM systems are evaluated, deployed, and monitored responsibly\u003c\/p\u003e\u003cp\u003eWhat Makes This Book Different\u003c\/p\u003e\u003cp\u003eMost books on large language models focus either on high-level descriptions or narrow implementation details. This book takes a \u003cstrong\u003efirst-principles, systems-oriented approach\u003c\/strong\u003e, emphasizing understanding over memorization and architecture over tools.\u003c\/p\u003e\u003cp\u003eThe examples use PyTorch for clarity, but the ideas are framework-agnostic and designed to remain relevant as tooling and architectures evolve. Clean diagrams, structured explanations, and carefully reasoned trade-offs replace hype and jargon.\u003c\/p\u003e\u003cp\u003eWho This Book Is For\u003c\/p\u003e\u003cp\u003eThis book is written for software engineers, data scientists, machine learning practitioners, researchers, and technically curious readers who want to move beyond surface familiarity with LLMs.\u003c\/p\u003e\u003cp\u003eYou do not need to be an expert in machine learning to begin, but you should be comfortable with programming and willing to engage with ideas thoughtfully. Readers looking for quick tutorials or platform-specific recipes may want supplementary resources; readers seeking durable understanding will find this book invaluable.\u003c\/p\u003e\u003cp\u003eWhat This Book Is Not\u003c\/p\u003e\u003cp\u003eThis book does not promise instant mastery, viral tricks, or platform-specific shortcuts. It does not focus on prompt engineering in isolation, nor does it attempt to catalog every model variant or benchmark.\u003c\/p\u003e\u003cp\u003eInstead, it focuses on what lasts: the principles that explain why large language models work-and how to think clearly about the systems built around them.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIf you want to understand modern large language models deeply-not just use them-this book provides the foundation.\u003c\/strong\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 460\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.93 x 11 x 8.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 15, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44200189362311,"sku":"9798232738587","price":57.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/gmVvpaAHSr9798232738587.webp?v=1773496218","url":"https:\/\/booksby.splitshops.com\/products\/modern-large-language-models-a-first-principles-guide-to-building-and-understanding-transformer-based-language-models-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}