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 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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Oswald Campesato 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 Learning).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
EUR 12,33 expédition depuis Etats-Unis vers France
Destinations, frais et délaisEUR 6,81 expédition depuis Etats-Unis vers France
Destinations, frais et délaisVendeur : California Books, Miami, FL, Etats-Unis
Etat : New. N° de réf. du vendeur I-9781501523564
Quantité disponible : Plus de 20 disponibles
Vendeur : Books From California, Simi Valley, CA, Etats-Unis
paperback. Etat : Very Good. Clean, unmarked copy. N° de réf. du vendeur mon0003719886
Quantité disponible : 1 disponible(s)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9781501523564_new
Quantité disponible : Plus de 20 disponibles
Vendeur : BargainBookStores, Grand Rapids, MI, Etats-Unis
Paperback or Softback. Etat : New. Large Language Models for Developers: A Prompt-Based Exploration of Llms 3.7. Book. N° de réf. du vendeur BBS-9781501523564
Quantité disponible : 5 disponible(s)
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : New. N° de réf. du vendeur 49799415-n
Quantité disponible : 15 disponible(s)
Vendeur : moluna, Greven, Allemagne
Etat : 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 . N° de réf. du vendeur 2183710054
Quantité disponible : Plus de 20 disponibles
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 49799415
Quantité disponible : 15 disponible(s)
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Etat : New. 2025. Paperback. . . . . . N° de réf. du vendeur V9781501523564
Quantité disponible : 15 disponible(s)
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
Paperback. Etat : New. 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 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. N° de réf. du vendeur LU-9781501523564
Quantité disponible : Plus de 20 disponibles
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Etat : New. N° de réf. du vendeur 49799415-n
Quantité disponible : Plus de 20 disponibles