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Optimization Algorithms for Distributed Machine Learning [electronic resource] / by Gauri Joshi.

By: Joshi, Gauri [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Learning, Networks, and Algorithms: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: XIII, 127 p. 40 illus., 38 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031190674.Subject(s): Algorithms | Machine learning | Artificial intelligence | Distribution (Probability theory) | Computer science | Algorithms | Machine Learning | Design and Analysis of Algorithms | Artificial Intelligence | Distribution Theory | Computer ScienceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 518.1 Online resources: Click here to access online
Contents:
Distributed 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.
In: Springer Nature eBookSummary: 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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Distributed 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.

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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