Distributed Machine Learning and Gradient Optimization
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Beschreibung
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.
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.
Presents a comprehensive overview of distributed machine learning Introduces the progress of gradient optimization for distributed machine learning Addresses the key challenge of implementing machine learning in the context of big data and large-scale systems
Autor*in
Jiawei Jiang
Themen in »Distributed Machine Learning and Gradient Optimization«
distributed machine learning gradient optimization parallelism gradient compression synchronization protocol