Large Language Models Handbook by Tech Sammy (6 results)
Language: English
Published by Roxanne Hamil Apr 2026, 2026
Series: Ai Guide Handbook collection, Book 10 of 15. Book 10 of 15 - Ai Guide Handbook collection
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Neuware.
- Softcover
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Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
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Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. Large Language Models have transformed the field of artificial intelligence. What began as research prototypes has evolved into powerful production systems driving real business value across industries. Yet moving from impressive demos to reliable, scalable, and cost-effective deployments re…mains one of the biggest challenges facing AI teams today.This handbook bridges that critical gap.Written for experienced developers, AI engineers, and technical leaders, Large Language Models (LLMs) Handbook delivers a complete, end-to-end guide to the modern LLM lifecycle. From transformer fundamentals and pre-training principles to advanced fine-tuning, preference alignment, retrieval-augmented generation (RAG), agentic systems, and production-grade inference optimization - every stage is covered with clarity, depth, and practical focus.You will learn how to: Build and train transformer-based models from scratchMaster parameter-efficient fine-tuning with LoRA, QLoRA, and DoRAAlign models using RLHF, DPO, ORPO, and other state-of-the-art techniquesDesign robust RAG systems - from naive retrieval to Graph RAG and Agentic RAGBuild autonomous agents with ReAct, tool use, memory, planning, and multi-agent collaborationOptimize inference with quantization (GPTQ, AWQ, GGUF), speculative decoding, and continuous batchingDeploy and serve models efficiently using vLLM, TensorRT-LLM, and OllamaEstablish production-grade LLMOps with monitoring, observability, versioning, and governanceNavigate security, ethics, cost optimization, and enterprise scaling challengesEach chapter combines rigorous technical explanations with complete, production-ready code examples, architectural diagrams, real-world production tips, hands-on exercises, and checklists you can apply immediately.Whether you are fine-tuning open-source models, building sophisticated agentic workflows, or scaling LLM applications to thousands of users, this handbook provides the practical knowledge and battle-tested patterns needed to succeed in 2026 and beyond.Perfect for: Machine Learning Engineers and AI PractitionersSoftware Engineers moving into AI systemsTechnical leaders and architects responsible for LLM initiativesAnyone building production-grade applications with Large Language ModelsStay ahead in one of the fastest-moving fields in technology. Master the full LLM lifecycle - from research to reliable production systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
