Optimization Algorithms for Distributed Machine Learning

Name: Optimization Algorithms for Distributed Machine Learning
Brand: Springer, Berlin
SKU: 9783031190698
Price: 46.66 EUR
Availability: InStock

Автор: Gauri Joshi

Език:

Корици: С меки корици

Издател: Springer, Berlin

Наличност: Външен склад

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46.66 € 91.26 лв

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine lear...

Информация за книгата

Автор

Gauri Joshi

Език

Английски език

Корици

Книга - С меки корици

Издадена

2023

страници

127

EAN

9783031190698

Enbook ID

44406882

Издател

Springer, Berlin

Теглоt

240

Размери

168 x 240

Пълно описание

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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