Editore: Springer International Publishing, 2022
ISBN 10: 3031190661 ISBN 13: 9783031190667
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 46,61
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Da: California Books, Miami, FL, U.S.A.
EUR 54,57
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Editore: Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022
ISBN 10: 3031190661 ISBN 13: 9783031190667
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 48,14
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 144 pp. Englisch.
Editore: Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 48,14
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Editore: Springer International Publishing, Springer Nature Switzerland, 2022
ISBN 10: 3031190661 ISBN 13: 9783031190667
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 48,14
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Editore: Springer International Publishing, Springer Nature Switzerland Nov 2023, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 48,14
Convertire valutaQuantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 144 pp. Englisch.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 54,34
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Da: Books Puddle, New York, NY, U.S.A.
EUR 62,27
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Aggiungi al carrelloCondizione: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
Da: Best Price, Torrance, CA, U.S.A.
EUR 44,04
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EUR 50,32
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Aggiungi al carrelloPF. Condizione: New.
EUR 67,51
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Aggiungi al carrelloCondizione: New. pp. 144.
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 69,26
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Aggiungi al carrelloPaperback. Condizione: Brand New. 140 pages. 9.45x6.61x0.33 inches. In Stock.
Editore: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 42,96
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where th.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 57,26
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Editore: Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022
ISBN 10: 3031190661 ISBN 13: 9783031190667
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 48,14
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime. 144 pp. Englisch.
Editore: Springer International Publishing, Springer Nature Switzerland Nov 2023, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 48,14
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime. 144 pp. Englisch.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 55,27
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Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: Majestic Books, Hounslow, Regno Unito
EUR 62,50
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Da: Majestic Books, Hounslow, Regno Unito
EUR 68,32
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 144.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 69,28
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 70,91
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 144.