Large language models generative de bill henry (4 resultados)

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  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798171920784

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

    Vendedor de 5 estrellas
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    Condición: Nuevo

    EUR 31,39

    Envío por EUR 4,83 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: Más de 20 disponibles

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

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798171920784

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    • Impresión bajo demanda

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

    Vendedor de 4 estrellas
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    Condición: Nuevo

    EUR 32,60

     Gastos de envío gratis 
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    Cantidad disponible: Más de 20 disponibles

    Condición: New. Print on Demand.

  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798171920784

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    • Impresión bajo demanda

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

    Vendedor de 5 estrellas
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    Condición: Nuevo

    EUR 36,51

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    Se envía dentro de Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Paperback. Condición: new. Paperback. Most AI tutorials stop at "look, it works." This book keeps going.Large Language Models and Generative AI is a hands-on guide to building real AI applications with Python - not toy demos, but systems that retrieve real information, call real tools, run real agents, and hold up under real users.You'll start with a single API call to an LLM and build outward, chapter by chapter: structured prompts, embeddings and RAG for grounding answers in your own data, tool calling and multi-step workflows, autonomous agents with memory and planning, and fine-tuning models to run locally. Then - where most books stop - you'll learn to evaluate what you've built, secure it against prompt injection and data leaks, and deploy it as a monitored, production-ready service.Every chapter closes with a project you actually build: a private knowledge assistant, a tool-using AI agent, a research agent, a security-tested application, and a complete generative AI system that ties everything together.What you'll walk away with: A working model of the full LLM application stack: prompts, RAG, tools, agents, evaluation, and securityPractical experience with embeddings, vector search, and retrieval-augmented generationThe ability to build and evaluate AI agents that plan, use tools, and hold stateFine-tuning and local model deployment with LoRA and QLoRAProduction skills: FastAPI backends, Docker deployment, cost control, and observabilityTen complete, working projects you can extend for your own portfolio or productWhy this book is different: most AI books teach you to prompt a model and call it finished. This one treats evaluation and security as core chapters, not afterthoughts - because an AI application nobody has tested and nobody has secured isn't done. It's just untested.Whether you're a developer moving into AI, an engineer tired of brittle demos, or someone who wants to ship something that actually works, this book maps the full path from first API call to production deployment.Scroll up and grab your copy to start building AI applications that go the distance. 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

    9798171920784

    • Tapa blanda
    • Impresión bajo demanda

    Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

    Vendedor de 5 estrellas
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    Condición: Nuevo

    EUR 35,88

    Envío por EUR 42,97 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Paperback. Condición: new. Paperback. Most AI tutorials stop at "look, it works." This book keeps going.Large Language Models and Generative AI is a hands-on guide to building real AI applications with Python - not toy demos, but systems that retrieve real information, call real tools, run real agents, and hold up under real users.You'll start with a single API call to an LLM and build outward, chapter by chapter: structured prompts, embeddings and RAG for grounding answers in your own data, tool calling and multi-step workflows, autonomous agents with memory and planning, and fine-tuning models to run locally. Then - where most books stop - you'll learn to evaluate what you've built, secure it against prompt injection and data leaks, and deploy it as a monitored, production-ready service.Every chapter closes with a project you actually build: a private knowledge assistant, a tool-using AI agent, a research agent, a security-tested application, and a complete generative AI system that ties everything together.What you'll walk away with: A working model of the full LLM application stack: prompts, RAG, tools, agents, evaluation, and securityPractical experience with embeddings, vector search, and retrieval-augmented generationThe ability to build and evaluate AI agents that plan, use tools, and hold stateFine-tuning and local model deployment with LoRA and QLoRAProduction skills: FastAPI backends, Docker deployment, cost control, and observabilityTen complete, working projects you can extend for your own portfolio or productWhy this book is different: most AI books teach you to prompt a model and call it finished. This one treats evaluation and security as core chapters, not afterthoughts - because an AI application nobody has tested and nobody has secured isn't done. It's just untested.Whether you're a developer moving into AI, an engineer tired of brittle demos, or someone who wants to ship something that actually works, this book maps the full path from first API call to production deployment.Scroll up and grab your copy to start building AI applications that go the distance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…