Noch Fragen? 0800 / 33 82 637

Optimization Algorithms for Distributed Machine Learning

Produktform: E-Buch Text Elektronisches Buch in proprietärem

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.weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-3-031-19067-4 / 978-3031190674 / 9783031190674

Verlag: Springer International Publishing

Erscheinungsdatum: 25.11.2022

Seiten: 127

Autor(en): Gauri Joshi

42,79 € inkl. MwSt.
Recommended Retail Price
kostenloser Versand

lieferbar - Lieferzeit 10-15 Werktage

zurück