Optimization Algorithms for Distributed Machine Learning

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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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ReiheSynthesis Lectures on Learning, Networks, and Algorithms
ISBN 9783031190667
Sprache Englisch
Ausgabe 1st ed. 2023
Erscheinungsdatum 26.11.2022
Umfang 127 Seiten
Genre Mathematik/Grundlagen
Format Hardcover
Verlag Springer International Publishing