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Distributed Machine Learning and Gradient Optimization [electronic resource] /

By: Contributor(s): Material type: TextTextSeries: Big Data ManagementPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2022Edition: 1st ed. 2022Description: XI, 169 p. 1 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811634208
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.31 23
LOC classification:
  • Q325.5-.7
Online resources:
Contents:
1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. .
In: Springer Nature eBookSummary: This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal toa broad audience in the field of machine learning, artificial intelligence, big data and database management.
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1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. .

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal toa broad audience in the field of machine learning, artificial intelligence, big data and database management.

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