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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Optimization Algorithms for Distributed Machine Learning

Optimization Algorithms for Distributed Machine Learning

Joshi, Gauri

Paperback

Series: Synthesis Lectures on Learning, Networks, and Algorithms

General ComputersProbability & StatisticsProgramming

ISBN10: 3031190696
ISBN13: 9783031190698
Publisher: Springer Nature
Published: Nov 26 2023
Pages: 127
Weight: 0.53
Height: 0.31 Width: 6.69 Depth: 9.61
Language: English
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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