Optimization Algorithms For Distributed Machine Learning
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Optimization Algorithms For Distributed Machine Learning
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.
- 品牌: Springer Nature B.V
- 类别: 计算机与互联网
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语言: English
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作者: Gauri Joshi
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出版日期: 2023-11-26
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页数: 144
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出版社／标签: Springer Nature B.V
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格式: Paperback
- Fruugo ID: 557202451-1125471851
- ISBN: 9783031190698
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