9781501523564 - large language models for developers: a prompt-based exploration of llms (mli generative ai series) de campesato, oswald (22 resultados)
- Tapa blanda
Librería: Books From California, Simi Valley, CA, Estados Unidos de AmericaBooks From California
Contactar con el vendedorVendedor de 4 estrellasCondición: Usado - Bueno
EUR 38,94
Envío por EUR 4,37Se envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
paperback. Condición: Very Good. Clean, unmarked copy.
- Tapa blanda
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de AmericaBargainBookStores
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 46,51
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 5 disponibles
Paperback or Softback. Condición: New. Large Language Models for Developers: A Prompt-Based Exploration of Llms. Book.
- Tapa blanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 57,08
Envío por EUR 8,85Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
- Tapa blanda
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 52,64
Envío por EUR 13,89Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. In.
- Tapa blanda
Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
Contactar con el vendedorVendedor de 3 estrellasCondición: Nuevo
EUR 68,64
Envío por EUR 13,50Se envía de Italia a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: new.
Idioma: Inglés
Editorial: Mercury Learning And Information, De Gruyter Jan 2025 2025
- Tapa blanda
Librería: Wegmann1855, Zwiesel, , AlemaniaWegmann1855
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 58,95
Envío por EUR 25,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. Neuware -This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learn…ing, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES¿ Covers the full lifecycle of working with LLMs, from model selection to deployment¿ Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization¿ Teaches readers to enhance model efficiency with advanced optimization techniques¿ Includes companion files with code and images -- available from the publisher.
- Tapa blanda
Librería: Majestic Books, Hounslow, , Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 81,00
Envío por EUR 7,54Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New.
- Tapa blanda
Librería: Revaluation Books, Exeter, , Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 75,98
Envío por EUR 14,49Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Paperback. Condición: Brand New. 1012 pages. 6.00x1.90x9.00 inches. In Stock.
- Tapa blanda
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 96,58
Envío por EUR 10,50Se envía de Irlanda a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. 2025. Paperback. . . . . .
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 110,09
Envío por EUR 3,49Se envía dentro de Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New.
Más imágenes- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 54,00
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. Large Language Models for Developers | A Prompt-based Exploration of LLMs | Oswald Campesato | Taschenbuch | MLI Generative AI Series | 1012 S. | Englisch | 2025 | Mercury Learning and Information | EAN 9781501523564 | Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthi…ner Str. 13, 10785 Berlin, productsafety[at]degruyterbrill[dot]com | Anbieter: preigu.
- Tapa blanda
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 123,32
Envío por EUR 9,19Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. 2025. Paperback. . . . . . Books ship from the US and Ireland.
Idioma: Inglés
Editorial: Mercury Learning And Information, De Gruyter Jan 2025 2025
- Tapa blanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 64,71
Envío por EUR 67,40Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. Neuware - This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine lear…ning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES- Covers the full lifecycle of working with LLMs, from model selection to deployment- Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization- Teaches readers to enhance model efficiency with advanced optimization techniques- Includes companion files with code and images -- available from the publisher.
Idioma: Inglés
Editorial: Mercury Learning And Information, De Gruyter Jan 2025 2025
- Tapa blanda
Librería: Books-by-Floh, Paderborn, AlemaniaBooks-by-Floh
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 81,59
Envío por EUR 105,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. Neuware -This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learn…ing, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES¿ Covers the full lifecycle of working with LLMs, from model selection to deployment¿ Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization¿ Teaches readers to enhance model efficiency with advanced optimization techniques¿ Includes companion files with code and images -- available from the publisher 1046 pp. Englisch.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 44,13
Envío por EUR 2,31Se envía dentro de Estados Unidos de AmericaCantidad disponible: 15 disponibles
Condición: New.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 48,61
Envío por EUR 2,31Se envía dentro de Estados Unidos de AmericaCantidad disponible: 15 disponibles
Condición: As New. Unread book in perfect condition.
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 52,63
Envío por EUR 17,39Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 57,56
Envío por EUR 17,39Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: Mercury Learning And Information, De Gruyter Jan 2025 2025
- Tapa blanda
- Impresión bajo demanda
Librería: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, , AlemaniaRheinberg-Buch Andreas Meier eK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 58,95
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for d…evelopers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES- Covers the full lifecycle of working with LLMs, from model selection to deployment- Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization- Teaches readers to enhance model efficiency with advanced optimization techniques- Includes companion files with code and images -- available from the publisher 1046 pp. Englisch.
Idioma: Inglés
Editorial: Mercury Learning And Information, De Gruyter Jan 2025 2025
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 58,95
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for d…evelopers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES- Covers the full lifecycle of working with LLMs, from model selection to deployment- Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization- Teaches readers to enhance model efficiency with advanced optimization techniques- Includes companion files with code and images -- available from the publisher 1046 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, , Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 51,60
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, Data Science, and Generative AI. He is the author/co-author of over forty-five books including Google Gemini for Python, Large Language Models…, and GPT-4 for Developers (all Mercury .
Idioma: Inglés
Editorial: Mercury Learning And Information, De Gruyter Jan 2025 2025
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 58,95
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for devel…opers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES¿ Covers the full lifecycle of working with LLMs, from model selection to deployment¿ Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization¿ Teaches readers to enhance model efficiency with advanced optimization techniques¿ Includes companion files with code and images -- available from the publisherWalter de Gruyter, Genthiner Straße 13, 10785 Berlin 1046 pp. Englisch.
