Articles liés à Optimization Algorithms for Distributed Machine Learning
Synopsis
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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À propos de l?auteur
Gauri Joshi, Ph.D., is an Associate Professor in the ECE department at Carnegie Mellon University. Dr. Joshi completed her Ph.D. from MIT EECS. Her current research is on designing algorithms for federated learning, distributed optimization, and parallel computing. Her awards and honors include being named as one of MIT Technology Review's 35 Innovators under 35 (2022), the NSF CAREER Award (2021), the ACM SIGMETRICS Best Paper Award (2020), Best Thesis Prize in Computer science at MIT (2012), and Institute Gold Medal of IIT Bombay (2010).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurSpringer International Publishing AG
- Date d'édition2022
- ISBN 10 3031190661
- ISBN 13 9783031190667
- ReliureRelié
- Langueanglais
- Nombre de pages127
- Coordonnées du fabricantnon disponible
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Optimization Algorithms for Distributed Machine Learning
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Springer International Publishing, 2022
ISBN 10 : 3031190661
ISBN 13 : 9783031190667
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Etat : Hervorragend. Zustand: Hervorragend | Seiten: 144 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 40959855/1
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Optimization Algorithms for Large-scale Machine Learning
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ISBN 10 : 3031190661
ISBN 13 : 9783031190667
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Gebunden. Etat : 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. N° de réf. du vendeur 706732221
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Optimization Algorithms for Distributed Machine Learning
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Springer International Publishing, Springer Nature Switzerland, 2022
ISBN 10 : 3031190661
ISBN 13 : 9783031190667
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Buch. Etat : 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. N° de réf. du vendeur 9783031190667
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Optimization Algorithms for Distributed Machine Learning
ISBN 10 : 3031190661
ISBN 13 : 9783031190667
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Buch. Etat : 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. N° de réf. du vendeur 9783031190667
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Optimization Algorithms for Distributed Machine Learning
ISBN 10 : 3031190661
ISBN 13 : 9783031190667
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9783031190667
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