Optimization Algorithms for Distributed Machine Learning

Joshi, Gauri

ISBN 10: 3031190688 ISBN 13: 9783031190681

Verlag: Springer, 2022

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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.

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Titel: Optimization Algorithms for Distributed ...
Verlag: Springer
Erscheinungsdatum: 2022
Einband: Softcover
Zustand: New

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Optimization Algorithms for Distributed Machine Learning

Joshi, Gauri

Verlag: Springer, 2022

ISBN 10: 3031190661 ISBN 13: 9783031190667

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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: B�cher | 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. Artikel-Nr. 40959855/1

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Optimization Algorithms for Distributed Machine Learning

Gauri Joshi

Verlag: Springer, 2023

ISBN 10: 3031190696 ISBN 13: 9783031190698

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Optimization Algorithms for Distributed Machine Learning

Gauri Joshi

Verlag: Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022

ISBN 10: 3031190661 ISBN 13: 9783031190667

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Buch. Zustand: 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. Artikel-Nr. 9783031190667

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Optimization Algorithms for Distributed Machine Learning

Gauri Joshi

Verlag: Springer International Publishing, Springer Nature Switzerland, 2023

ISBN 10: 3031190696 ISBN 13: 9783031190698

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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

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Taschenbuch. Zustand: 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. Artikel-Nr. 9783031190698

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Optimization Algorithms for Distributed Machine Learning

Gauri Joshi

Verlag: Springer International Publishing, Springer Nature Switzerland Nov 2023, 2023

ISBN 10: 3031190696 ISBN 13: 9783031190698

Neu Taschenbuch

Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland

Verk�uferbewertung 5 von 5 Sternen

Taschenbuch. Zustand: 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. Artikel-Nr. 9783031190698

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Optimization Algorithms for Distributed Machine Learning

Gauri Joshi

Verlag: Springer International Publishing, Springer Nature Switzerland, 2022

ISBN 10: 3031190661 ISBN 13: 9783031190667

Neu Hardcover

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verk�uferbewertung 5 von 5 Sternen

Buch. Zustand: 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. Artikel-Nr. 9783031190667

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