Isbn: 9798250073844 - building llm systems with rag: from deep learning to scalable generative ai in production with langchain and ollama (9 resultados)

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  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798250073844

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    Editorial: Independently published, 2026

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    Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices

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    Editorial: Independently Published, 2026

    9798250073844

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  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798250073844

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    Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK

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    PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798250073844

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    Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK

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  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798250073844

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    Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK

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  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798250073844

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    Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail

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    Paperback. Condición: new. Paperback. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) are redefining how software systems are built. But most resources either focus on theory - or on shallow demos.This book bridges the gap.Building LLM Systems with RAG takes you from Machine Learning fundamentals to deploying scalable, production-ready Generative AI systems using modern tools like LangChain and Ollama.This is not just another prompt engineering guide.This is a system-building handbook.What You'll LearnYou will build a complete mental model of modern AI systems: Foundations of Machine Learning and Deep LearningNeural Networks, Transformers, and LLM architecturePrompt Engineering techniques used in real systemsHow RAG reduces hallucinations and improves reliabilityEmbeddings and vector databasesChunking strategies that impact retrieval qualityHybrid search (Sparse + Dense retrieval)Reranking techniques for precisionEvaluating RAG systems properlyDesigning production-ready LLM pipelinesDeploying scalable RAG systems using LangChain and OllamaRunning Local AI models securely and cost-effectivelyBy the end of this book, you won't just understand LLMs - you'll know how to build reliable AI systems around them.Who This Book Is ForThis book is for: Software EngineersMachine Learning EngineersAI ArchitectsTechnical FoundersDevelopers moving into Generative AIYou must know Python not Perfessional but minimum syntax understanding.No PhD required - but curiosity and technical mindset are essential.From Deep Learning to ProductionYou will move step-by-step: Machine Learning Deep Learning Transformers Large Language Models Prompt Engineering Basic RAG Advanced RAG Production DeploymentEach concept builds toward one goal: Creating scalable, production-grade LLM systems.What Makes This Book Different?Unlike many AI books: It focuses on systems, not just modelsIt explains why architectural decisions matterIt includes production engineering considerationsIt combines theory with practical designIt uses real-world RAG pipelinesIt integrates LangChain and Ollama for local AIThis book prepares you for the real world - not just the demo environment. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798250073844

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    Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books

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    EUR 22,61

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  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798250073844

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    Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

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    EUR 25,14

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    Cantidad disponible: 1 disponibles

    Paperback. Condición: new. Paperback. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) are redefining how software systems are built. But most resources either focus on theory - or on shallow demos.This book bridges the gap.Building LLM Systems with RAG takes you from Machine Learning fundamentals to deploying scalable, production-ready Generative AI systems using modern tools like LangChain and Ollama.This is not just another prompt engineering guide.This is a system-building handbook.What You'll LearnYou will build a complete mental model of modern AI systems: Foundations of Machine Learning and Deep LearningNeural Networks, Transformers, and LLM architecturePrompt Engineering techniques used in real systemsHow RAG reduces hallucinations and improves reliabilityEmbeddings and vector databasesChunking strategies that impact retrieval qualityHybrid search (Sparse + Dense retrieval)Reranking techniques for precisionEvaluating RAG systems properlyDesigning production-ready LLM pipelinesDeploying scalable RAG systems using LangChain and OllamaRunning Local AI models securely and cost-effectivelyBy the end of this book, you won't just understand LLMs - you'll know how to build reliable AI systems around them.Who This Book Is ForThis book is for: Software EngineersMachine Learning EngineersAI ArchitectsTechnical FoundersDevelopers moving into Generative AIYou must know Python not Perfessional but minimum syntax understanding.No PhD required - but curiosity and technical mindset are essential.From Deep Learning to ProductionYou will move step-by-step: Machine Learning Deep Learning Transformers Large Language Models Prompt Engineering Basic RAG Advanced RAG Production DeploymentEach concept builds toward one goal: Creating scalable, production-grade LLM systems.What Makes This Book Different?Unlike many AI books: It focuses on systems, not just modelsIt explains why architectural decisions matterIt includes production engineering considerationsIt combines theory with practical designIt uses real-world RAG pipelinesIt integrates LangChain and Ollama for local AIThis book prepares you for the real world - not just the demo environment. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…