Fengxiang he (22 resultados)
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Books From California, Simi Valley, CA, Estados Unidos de AmericaBooks From California
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EUR 83,24
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hardcover. Condición: Very Good. Cover and edges may have some wear.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 132,33
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Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 136,37
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 134,93
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 151,02
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 144,30
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 154,31
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Condición: New. In.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 171,43
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 170,59
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Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 170,78
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Condición: As New. Unread book in perfect condition.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 201,50
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Condición: New.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 157,86
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Idioma: Inglés
Editorial: Springer, Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 157,86
Envío por EUR 63,14Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a 'cloud' to conventional complexity-based learning th…eory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues.The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the 'effective' hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability.We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.
Idioma: Inglés
Editorial: Springer-Nature New York Inc, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 225,67
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Hardcover. Condición: Brand New. 306 pages. 9.25x6.10x9.21 inches. In Stock.
Idioma: Inglés
Editorial: Springer, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 118,26
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Condición: new. Questo è un articolo print on demand.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 149,79
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 308 pp. Englisch.
Idioma: Inglés
Editorial: Springer Nature Singapore Mär 2025, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 149,79
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a 'cloud' to conventional complexity-b…ased learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues.The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the 'effective' hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability.We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory. 284 pp. Englisch.
Idioma: Inglés
Editorial: Springer Nature Singapore, 2024
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: moluna, Greven, Alemaniamoluna
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EUR 127,40
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first comprehensive overview book on the foundations of deep learningWritten by leading experts in the fieldExplicates excellent generalizability of deep learning, including generalization analysisDeep learning ha…s si.
Idioma: Inglés
Editorial: Springer, Springer Feb 2025, 2025
Serie: Machine Learning: Foundations, Methodologies, and Applications, Libro 4 de 4. Libro 4 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 149,79
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a 'cloud' to conventional complexity-based… learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues.The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the 'effective' hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability.We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 308 pp. Englisch.
Idioma: Inglés
Editorial: Springer, Springer Nature Singapore Feb 2026, 2026
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 149,79
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 -Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a 'cloud' to conventional complexit…y-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues.The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the 'effective' hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability.We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 308 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 213,41
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Condición: New. Print on Demand.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 213,48
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
