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Ajouter au panierEtat : New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Ajouter au panierTaschenbuch. Etat : Neu. Accelerated Optimization for Machine Learning | First-Order Algorithms | Zhouchen Lin (u. a.) | Taschenbuch | xxiv | Englisch | 2021 | Springer Singapore | EAN 9789811529122 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Edité par Springer Nature Singapore, Springer Nature Singapore Mai 2020, 2020
ISBN 10 : 9811529094 ISBN 13 : 9789811529092
Langue: anglais
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Ajouter au panierBuch. Etat : Neu. Neuware -This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 300 pp. Englisch.
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Ajouter au panierHardcover. Etat : New. New. book.
Edité par Springer Nature Singapore, 2021
ISBN 10 : 9811529124 ISBN 13 : 9789811529122
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where thealgorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
Edité par Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10 : 9811529094 ISBN 13 : 9789811529092
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where thealgorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
Edité par Springer-Nature New York Inc, 2021
ISBN 10 : 9811529124 ISBN 13 : 9789811529122
Langue: anglais
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Ajouter au panierPaperback. Etat : Brand New. 299 pages. 9.25x6.10x0.63 inches. In Stock.
Edité par Springer-Nature New York Inc, 2020
ISBN 10 : 9811529094 ISBN 13 : 9789811529092
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 275 pages. 9.75x6.50x0.75 inches. In Stock.
Edité par Machinery Industry Press, 2021
ISBN 10 : 7111685008 ISBN 13 : 9787111685005
Langue: chinois
Vendeur : liu xing, Nanjing, JS, Chine
EUR 126,02
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Ajouter au panierpaperback. Etat : New. Paperback. Pub Date: 2021-07-01 Pages: 264 Language: Chinese Publisher: Machinery Industry Press. Machine learning is a discipline about building predictive or descriptive models from data to improve machine problem-solving capabilities.?After the model is established. an appropriate optimization algorithm is needed to solve the parameters of the model. Therefore. the optimization algorithm is an important part of machine learning.?However. traditional optimization algorithms are not complete.
Edité par Springer Nature Singapore Mai 2021, 2021
ISBN 10 : 9811529124 ISBN 13 : 9789811529122
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where thealgorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. 275 pp. Englisch.
Edité par Springer Nature Singapore Mai 2020, 2020
ISBN 10 : 9811529094 ISBN 13 : 9789811529092
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 160,49
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Ajouter au panierBuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where thealgorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. 300 pp. Englisch.
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first monograph on accelerated first-order optimization algorithms used in machine learningIncludes forewords by Michael I. Jordan, Zongben Xu, and Zhi-Quan Luo, and written by experts on machine learning and optimization.
Edité par Springer, Berlin|Springer Nature Singapore|Springer, 2021
ISBN 10 : 9811529124 ISBN 13 : 9789811529122
Langue: anglais
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order opt.
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Ajouter au panierBuch. Etat : Neu. Accelerated Optimization for Machine Learning | First-Order Algorithms | Zhouchen Lin (u. a.) | Buch | xxiv | Englisch | 2020 | Springer Singapore | EAN 9789811529092 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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