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Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms) - Tapa dura
Sinopsis
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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Acerca del autor
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).
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- EditorialSpringer-Verlag GmbH
- Año de publicación2022
- ISBN 10 3031190661
- ISBN 13 9783031190667
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas144
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Optimization Algorithms for Distributed Machine Learning
Publicado por
Springer International Publishing, 2022
ISBN 10: 3031190661
ISBN 13: 9783031190667
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Librería: Buchpark, Trebbin, Alemania
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Condición: Hervorragend. Zustand: Hervorragend | Seiten: 144 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 40959855/1
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Optimization Algorithms for Distributed Machine Learning
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Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022
ISBN 10: 3031190661
ISBN 13: 9783031190667
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Buch. Condición: 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. Nº de ref. del artículo: 9783031190667
Cantidad disponible: 2 disponibles
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Optimization Algorithms for Distributed Machine Learning
Publicado por
Springer International Publishing, Springer Nature Switzerland, 2022
ISBN 10: 3031190661
ISBN 13: 9783031190667
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783031190667
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Optimization Algorithms for Large-scale Machine Learning
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Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031190661
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Gebunden. Condición: 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. Nº de ref. del artículo: 706732221
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Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand This item is printed on demand. Nº de ref. del artículo: 401162760
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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. 1st ed. 2023 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26396295639
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Optimization Algorithms for Distributed Machine Learning
Publicado por
Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022
ISBN 10: 3031190661
ISBN 13: 9783031190667
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783031190667
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Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18396295645
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