Artículos relacionados a Foundations of Deep Learning (Machine Learning: Foundations,...
Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications) - Tapa dura
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
Comprar nuevo
Ver este artículo
EUR 123,53
EUR 19,49 gastos de envío desde Alemania a España
Destinos, gastos y plazos de envíoResultados de la búsqueda para Foundations of Deep Learning (Machine Learning: Foundations,...
Imagen del vendedor
Foundations of Deep Learning
Publicado por
Springer Nature Singapore, 2024
ISBN 10: 9811682321
ISBN 13: 9789811682322
Nuevo
Tapa dura
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. 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 has si. Nº de ref. del artículo: 521847426
Cantidad disponible: 1 disponibles
Imagen de archivo
Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9789811682322_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Foundations of Deep Learning
Publicado por
Springer Nature Singapore Mär 2025, 2025
ISBN 10: 9811682321
ISBN 13: 9789811682322
Nuevo
Tapa dura
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
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-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. 284 pp. Englisch. Nº de ref. del artículo: 9789811682322
Cantidad disponible: 2 disponibles
Imagen del vendedor
Foundations of Deep Learning
Publicado por
Springer Nature Singapore, Springer Nature Singapore, 2025
ISBN 10: 9811682321
ISBN 13: 9789811682322
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
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 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. Nº de ref. del artículo: 9789811682322
Cantidad disponible: 1 disponibles
Imagen de archivo
Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9789811682322
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Foundations of Deep Learning
Publicado por
Springer Nature Singapore, Springer Nature Singapore Feb 2025, 2025
ISBN 10: 9811682321
ISBN 13: 9789811682322
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch. Nº de ref. del artículo: 9789811682322
Cantidad disponible: 2 disponibles
Imagen de archivo
Foundations of Deep Learning (Hardcover)
Publicado por
Springer Verlag, Singapore, Singapore, 2025
ISBN 10: 9811682321
ISBN 13: 9789811682322
Nuevo
Tapa dura
Librería: CitiRetail, Stevenage, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9789811682322
Cantidad disponible: 1 disponibles
Imagen de archivo
Foundations of Deep Learning (Hardcover)
Publicado por
Springer Verlag, Singapore, Singapore, 2025
ISBN 10: 9811682321
ISBN 13: 9789811682322
Nuevo
Tapa dura
Librería: AussieBookSeller, Truganina, VIC, Australia
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9789811682322
Cantidad disponible: 1 disponibles
Imagen de archivo
Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26396346908
Cantidad disponible: 4 disponibles
Imagen de archivo
Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Print on Demand. Nº de ref. del artículo: 401111491
Cantidad disponible: 4 disponibles