9783031767692 - introduction to foundation models di chen, pin-yu; liu, sijia (17 risultati)
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Da: Books From California, Simi Valley, CA, U.S.A.Books From California
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hardcover. Condizione: Good. Cover and edges may have some wear.
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 323 pages. 9.26x6.11x9.21 inches. In Stock.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to… the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models:Part Iintroduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes withthe intricacies of transformers and in-context learning.Part IIintroduces automated visual prompting techniques,prompting LLMs with privacy,memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used tobenchmark foundation models, and elucidates machine unlearning for foundation models.Part IIIprovides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models.Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condizione: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding a…nd contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models:Part Iintroduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes withthe intricacies of transformers and in-context learning.Part IIintroduces automated visual prompting techniques,prompting LLMs with privacy,memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used tobenchmark foundation models, and elucidates machine unlearning for foundation models.Part IIIprovides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models.Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field. 324 pp. Englisch.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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Hardcover. Condizione: new. Hardcover. This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more tr…ustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models:Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning.Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models.Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models.Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 116,33
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Condizione: New. PRINT ON DEMAND.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 80,24
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and c…ontribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models:Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning.Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models.Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models.Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 324 pp. Englisch.
