Large language models di zhao wayne (11 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2026

    9819662583 / 9789819662586

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  • Lingua: Inglese

    Editore: Springer, 2026

    9819662583 / 9789819662586

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  • Lingua: Inglese

    Editore: Springer, 2026

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  • Lingua: Inglese

    Editore: Springer, 2026

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  • Lingua: Inglese

    Editore: Springer, 2026

    9819662583 / 9789819662586

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  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, CH, 2026

    9819662583 / 9789819662586

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    Hardback. Condizione: New. Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs)? This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs.This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks.Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject.The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book.

  • Lingua: Inglese

    Editore: Springer-Nature New York Inc, 2025

    9819662583 / 9789819662586

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    Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books

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    EUR 306,70

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    Hardcover. Condizione: Brand New. 391 pages. 9.25x6.10x10.00 inches. In Stock.

  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, CH, 2026

    9819662583 / 9789819662586

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    Hardback. Condizione: New. Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs)? This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs.This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks.Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject.The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book.

  • Lingua: Inglese

    Editore: Springer Nature Singapore Mär 2026, 2026

    9819662583 / 9789819662586

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    EUR 213,99

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    Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs) This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs.This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks.Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject.The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book. 466 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer, Springer Feb 2026, 2026

    9819662583 / 9789819662586

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    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs) This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs.This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks.Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject.The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 488 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, Cham, 2026

    9819662583 / 9789819662586

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    EUR 241,72

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    Hardcover. Condizione: new. Hardcover. Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs)? This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs.This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks.Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject.The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.