Kniha Optimization Algorithms for Distributed Machine Learning Gauri Joshi

Optimization Algorithms for Distributed Machine Learning

Autor: Gauri Joshi
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: Springer, Berlin
Dostupnost: Skladem u dodavatele
Odesíláme za 5-8 dnů
1 009
This book discusses state-of-the-art stochastic optimization algorithms for distributed machine lear...

Informace o knize

Autor
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2023
Stránek
127
EAN
9783031190698
Enbook ID
44406882
Vydavatel
Hmotnost
240
Rozměry
168 x 240

Kompletní popis

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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