{"product_id":"9798868827846","title":"Transformers and Large Language Models: A Hands-On Guide to RAG and Agentic AI","description":"\u003ch1\u003eTransformers and Large Language Models: A Hands-On Guide to RAG and Agentic AI\u003c\/h1\u003e \u003ch2\u003eGad, Ahmed Fawzy\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eThis book is a hands-on guide to understanding the foundations, architectures, and real-world applications of transformers and large language models in modern AI.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eThe book begins by laying the foundations of generative AI architectures,  tokenization, encoding, and classical modeling techniques. Initial chapters address the evolution from feed-forward networks and recurrent neural networks to long short-term memory (LSTM), setting the stage for the revolutionary transformer architecture. The core of the book focuses on transformers, introducing the encoder-decoder framework, attention mechanisms, positional encodings, and the internal workings of multi-head attention, normalization, and multi-layer perceptrons. Readers gain insight into advanced techniques such as rotary positional embeddings (RoPE), mixture of experts (MoE), and knowledge distillation, alongside practical training strategies like self-supervised learning, fine-tuning, and reinforcement learning with human feedback. Popular models from OpenAI, DeepSeek, and other vendors are examined to highlight the evolution of the LLM landscape. Building on these foundations, the text explores methods for model customization, including parameter-efficient fine-tuning (LoRA, adapters), text generation strategies, prompt engineering, and quantization. Retrieval-Augmented Generation (RAG) is introduced as a critical innovation for grounding LLMs in external knowledge, with detailed evaluation techniques for retrieval and generation. Finally, the book ventures into Agentic AI, demonstrating protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) interactions with practical coding examples.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eIn conclusion, this book serves as both a practical guide, equipping readers with the technical depth and applied strategies needed to design, fine-tune, and deploy cutting-edge transformers and large language models for real-world applications.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eWhat we will learn:\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpFirst\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: Wingdings; mso-fareast-font-family: Wingdings; mso-bidi-font-family: Wingdings;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003eØ\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e  \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eUnderstand the foundations of AI, ML pipelines, tokenization, encoding, and early neural architectures.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpMiddle\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: Wingdings; mso-fareast-font-family: Wingdings; mso-bidi-font-family: Wingdings;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003eØ\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e  \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eExplore transformers in depth—encoder-decoder design, attention mechanisms, and advanced embedding methods.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpMiddle\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: Wingdings; mso-fareast-font-family: Wingdings; mso-bidi-font-family: Wingdings;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003eØ\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e  \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eLearn modern LLM advancements like RoPE, MoE, SLMs, fine-tuning strategies, and evaluation techniques.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpLast\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: Wingdings; mso-fareast-font-family: Wingdings; mso-bidi-font-family: Wingdings;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003eØ\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e  \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eMaster practical customization through prompt engineering, PEFT methods, quantization, and text generation.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpLast\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003enWho this book is for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpLast\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e\u003cspan style=\"font-size: 12.0pt; line-height: 106%; font-family: 'Times New Roman',serif;\"\u003eData scientists, ML engineers, AI researchers, and developers exploring Transformers and large language models.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpLast\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e \u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpLast\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e \u003c\/p\u003e\n\u003cp class=\"MsoListParagraphCxSpLast\" style=\"text-indent: -.25in; mso-list: l0 level1 lfo1;\"\u003e \u003c\/p\u003e\n\u003cp class=\"MsoNormal\"\u003e \u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-07-30\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9798868827846\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: 254cm x178cm\u003c\/p\u003e \u003cp\u003ePages: 181\u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":46921238806668,"sku":"9798868827846","price":53.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9798868827846.jpg?v=1779973795","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9798868827846","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}