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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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About this product
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
