Da: SpringBooks, Berlin, Germania
Prima edizione
EUR 27,56
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Aggiungi al carrelloHardcover. Condizione: Very Good. 1. Auflage. unread, some shelfwear.
EUR 156,40
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 156,40
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Editore: Springer Nature Singapore, Springer Nature Singapore Mai 2020, 2020
ISBN 10: 9811529094 ISBN 13: 9789811529092
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 160,49
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Aggiungi al carrelloBuch. Condizione: 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.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 162,91
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Editore: Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811529094 ISBN 13: 9789811529092
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 168,73
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Aggiungi al carrelloBuch. Condizione: 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.
EUR 196,89
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Da: California Books, Miami, FL, U.S.A.
EUR 196,89
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Da: Books Puddle, New York, NY, U.S.A.
EUR 204,91
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Aggiungi al carrelloCondizione: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Da: Books Puddle, New York, NY, U.S.A.
EUR 205,17
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 159,76
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Aggiungi al carrelloCondizione: New.
EUR 160,17
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Aggiungi al carrelloCondizione: New.
EUR 207,62
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Aggiungi al carrelloHardcover. Condizione: New. New. book.
Editore: Springer-Nature New York Inc, 2021
ISBN 10: 9811529124 ISBN 13: 9789811529122
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 235,53
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Aggiungi al carrelloPaperback. Condizione: Brand New. 299 pages. 9.25x6.10x0.63 inches. In Stock.
Editore: Springer-Nature New York Inc, 2020
ISBN 10: 9811529094 ISBN 13: 9789811529092
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 238,06
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Aggiungi al carrelloHardcover. Condizione: Brand New. 275 pages. 9.75x6.50x0.75 inches. In Stock.
Da: dsmbooks, Liverpool, Regno Unito
EUR 224,82
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Da: liu xing, Nanjing, JS, Cina
EUR 127,01
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Aggiungi al carrellopaperback. Condizione: 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.
Da: moluna, Greven, Germania
EUR 136,16
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Aggiungi al carrelloCondizione: 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.
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2021
ISBN 10: 9811529124 ISBN 13: 9789811529122
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 137,26
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Aggiungi al carrelloCondizione: 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.
Editore: Springer Nature Singapore Mai 2021, 2021
ISBN 10: 9811529124 ISBN 13: 9789811529122
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 160,49
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Editore: Springer Nature Singapore Mai 2020, 2020
ISBN 10: 9811529094 ISBN 13: 9789811529092
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 160,49
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Aggiungi al carrelloBuch. Condizione: 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.
Da: Majestic Books, Hounslow, Regno Unito
EUR 210,34
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Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Majestic Books, Hounslow, Regno Unito
EUR 210,45
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 218,36
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 218,42
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.