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Synopsis
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...
- PublisherSpringer
- Publication date2022
- ISBN 10 3031190688
- ISBN 13 9783031190681
- BindingPaperback
- LanguageEnglish
- Number of pages144
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