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Optimization Algorithms Distributed Machine by Joshi Gauri (16 results)
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Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 40.42
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£ 39.96
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Optimization Algorithms for Distributed Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 45.62
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Condition: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Language: English
£ 42.11
Convert currency£ 24.86 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: 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.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer International Publishing, Springer Nature Switzerland, 2022
ISBN 10: 3031190661 ISBN 13: 9783031190667
Language: English
£ 42.11
Convert currency£ 25.60 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketBuch. Condition: 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.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer-Nature New York Inc, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Language: English
£ 58.81
Convert currency£ 10 shipping from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 140 pages. 9.45x6.61x0.33 inches. In Stock.
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Optimization Algorithms for Distributed Machine Learning (Paperback)
Published by Springer International Publishing AG, Cham, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Language: English
£ 43.99
Convert currency£ 37 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer Nature B.V., 2022
ISBN 10: 3031190688 ISBN 13: 9783031190681
Language: English
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer Nature B.V., 2022
ISBN 10: 3031190688 ISBN 13: 9783031190681
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
£ 46.40
Convert currency£ 3.29 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer International Publishing, Springer Nature Switzerland Nov 2023, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 42.11
Convert currency£ 19.53 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: 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.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022
ISBN 10: 3031190661 ISBN 13: 9783031190667
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 42.11
Convert currency£ 19.53 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketBuch. Condition: 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.
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£ 60.73
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Add to basketCondition: New. Print on Demand This item is printed on demand.
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Condition: New. PRINT ON DEMAND.
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Optimization Algorithms for Distributed Machine Learning
Published by Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031190696 ISBN 13: 9783031190698
Language: English
£ 37.58
Convert currency£ 41.61 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketKartoniert / Broschiert. Condition: 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.
