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Distributed Machine Learning and Gradient Optimization / by Jiawei Jiang, Bin Cui, Ce Zhang
(Big Data Management. ISSN:25220187)

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2022
大きさ	XI, 169 p. 1 illus : online resource
著者標目	*Jiang, Jiawei author Cui, Bin author Zhang, Ce author SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0003193	Computer Scinece R0 2005-6,2022-3				9789811634208

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一般注記	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 to a broad audience in the field of machine learning, artificial intelligence, big data and database management HTTP:URL=https://doi.org/10.1007/978-981-16-3420-8
件　名	LCSH:Machine learning LCSH:Data mining LCSH:Database management FREE:Machine Learning FREE:Data Mining and Knowledge Discovery FREE:Database Management
分　類	LCC:Q325.5-.7 DC23:006.31
書誌ID	EB00002581
ISBN	9789811634208