Electronic Resource

Distributed Machine Learning and Gradient Optimization

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Penilaian

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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 appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2008000257	EB0004755	005.74 Jia d	Central Library (Database Springer E-Book)	Available

Detail Information

Series Title

Big Data Management

Call Number

005.74 Jia d

Publisher

Singapore : Springer Singapore., 2022

Collation

xi, 169p.: Ill.

Language

English

ISBN/ISSN

978-981-16-3420-8

Classification

005.74

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Data mining

Specific Detail Info

-

Statement of Responsibility

RTS

Other version/related

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