Distributed Machine Learning and Gradient Optimization
Jiang, Jiawei
Distributed Machine Learning and Gradient Optimization by Jiawei Jiang, Bin Cui, Ce Zhang - First edition 2022 - 1 recurso en línea (XI, 169 páginas) 1 ilustraciones - Big Data Management 2522-0187 .
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.
9789811634208
10.1007/978-981-16-3420-8 doi
Aprendizaje automático
Optimización matemática
Q325.5 / 2022 EB
Distributed Machine Learning and Gradient Optimization by Jiawei Jiang, Bin Cui, Ce Zhang - First edition 2022 - 1 recurso en línea (XI, 169 páginas) 1 ilustraciones - Big Data Management 2522-0187 .
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.
9789811634208
10.1007/978-981-16-3420-8 doi
Aprendizaje automático
Optimización matemática
Q325.5 / 2022 EB