Distributed Machine Learning and Gradient Optimization (Big Data Management) - Hardcover

Jiang, Jiawei; Cui, Bin; Zhang, Ce

 
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Synopsis

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...

About the Author

Jiawei Jiang obtained his PhD from Peking University 2018, advised by Prof. Bin Cui. His research interests include distributed machine learning, gradient optimization and automatic machine learning. He has served as a program committee member or reviewer for various international events, including SIGMOD, VLDB, ICDE, KDD, AAAI and TKDE. He was awarded the CCF Outstanding Doctoral Dissertation Award (2019) and ACM China Doctoral Dissertation Award (2018).

Bin Cui is...

From the Back Cover

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...

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  • PublisherSpringer
  • Publication date2022
  • ISBN 10 981163419X
  • ISBN 13 9789811634192
  • BindingHardcover
  • LanguageEnglish
  • Edition number1
  • Number of pages180

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Featured Edition

ISBN 10:  981163422X ISBN 13:  9789811634222
Publisher: Springer, 2023
Softcover