Subbaiah kalpa (12 risultati)
- Brossura
Da: California Books, Miami, FL, U.S.A.California Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 47,60
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
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Da: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 49,37
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: New. The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the…entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation Build and optimize RAG pipelines with effective retrieval strategies and vector databases Deploy optimized LLMs using quantization techniques and scalable inference frameworks Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start.
- Brossura
Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 76,91
Spedizione gratuitaSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: New. The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the…entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation Build and optimize RAG pipelines with effective retrieval strategies and vector databases Deploy optimized LLMs using quantization techniques and scalable inference frameworks Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start.
- Brossura
Da: Wegmann1855, Zwiesel, GermaniaWegmann1855
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 64,19
EUR 25,95 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware -The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mas…tering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial 'last mile' of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:- Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework - Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation - Build and optimize RAG pipelines with effective retrieval strategies and vector databases - Deploy optimized LLMs using quantization techniques and scalable inference frameworks - Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start.
- Brossura
Da: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 52,25
EUR 42,79 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: New. The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the…entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation Build and optimize RAG pipelines with effective retrieval strategies and vector databases Deploy optimized LLMs using quantization techniques and scalable inference frameworks Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start.
- Brossura
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 76,99
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: New.
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 64,19
EUR 64,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware - The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to ma…stering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial 'last mile' of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation.
- Brossura
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 72,33
EUR 75,87 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: New. The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the…entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation Build and optimize RAG pipelines with effective retrieval strategies and vector databases Deploy optimized LLMs using quantization techniques and scalable inference frameworks Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start.
- Brossura
Da: Books-by-Floh, Paderborn, GermaniaBooks-by-Floh
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 64,19
EUR 105,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware -The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mas…tering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial 'last mile' of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:- Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework - Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation - Build and optimize RAG pipelines with effective retrieval strategies and vector databases - Deploy optimized LLMs using quantization techniques and scalable inference frameworks - Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start 428 pp. Englisch.
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- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 64,19
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory,… this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial 'last mile' of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation 402 pp. Englisch.
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- Print on Demand
Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermaniaRheinberg-Buch Andreas Meier eK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 64,19
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory,… this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial 'last mile' of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.What you will learn:Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation 428 pp. Englisch.
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- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 64,19
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, thi…s book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.The book doesn't stop at training. It tackles the crucial 'last mile' of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn:- Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework - Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation - Build and optimize RAG pipelines with effective retrieval strategies and vector databases - Deploy optimized LLMs using quantization techniques and scalable inference frameworks - Develop multimodal and agentic AI applications with vision-language models and autonomous agents Who this book is for:This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and startSpringer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 428 pp. Englisch.
