Optimization Algorithms for Distributed Machine Learning - Brossura

Joshi, Gauri

Brossura

ISBN 10: 3031190688 ISBN 13: 9783031190681

Casa editrice: Springer, 2022

Riassunto

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.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3031190688
• ISBN 13: 9783031190681
• Rilegatura: Copertina flessibile
• Numero di pagine: 144
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania