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portada Optimization Algorithms for Distributed Machine Learning
Type
Physical Book
Author
Publisher
Language
English
Pages
127
Format
Hardcover
Dimensions
23.6 x 16.2 x 1.0 cm
Weight
0.39 kg.
ISBN13
9783031190667
Edition No.
1
Categories

Optimization Algorithms for Distributed Machine Learning

Gauri Joshi (Author) · Springer · Hardcover

Optimization Algorithms for Distributed Machine Learning - Joshi, Gauri

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Synopsis "Optimization Algorithms for Distributed Machine Learning"

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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