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Synthesis Lectures on Learning, Networks, and Algorithms Ser.: Optimization Algorithms for Distributed Machine Learning by Gauri Joshi (2023, Trade Paperback)

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Author Gauri Joshi. 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.

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Product Identifiers

PublisherSpringer International Publishing A&G

ISBN-103031190696

ISBN-139783031190698

eBay Product ID (ePID)16064276340

Product Key Features

Number of PagesXiii, 127 Pages

LanguageEnglish

Publication NameOptimization Algorithms for Distributed Machine Learning

SubjectProgramming / Algorithms, Intelligence (Ai) & Semantics, Probability & Statistics / General, General

Publication Year2023

TypeTextbook

AuthorGauri Joshi

Subject AreaMathematics, Computers, Science

SeriesSynthesis Lectures on Learning, Networks, and Algorithms Ser.

FormatTrade Paperback

Dimensions

Item Weight9 Oz

Item Length9.4 in

Item Width6.6 in

Additional Product Features

Dewey Edition23

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal006.31015196

Table Of ContentDistributed Optimization in Machine Learning.- Calculus, Probability and Order Statistics Review.- Convergence of SGD and Variance-Reduced Variants.- Synchronous SGD and Straggler-Resilient Variants.- Asynchronous SGD and Staleness-Reduced Variants.- Local-update and Overlap SGD.- Quantized and SparsiFied Distributed SGD.- Decentralized SGD and its Variants.

SynopsisThis 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.

LC Classification NumberQA76.9.A43