TY  - GEN
AB  - 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.
AU  - Jiang, Jiawei,
AU  - Cui, Bin,
AU  - Zhang, Ce,
CN  - Q325.5
CY  - Singapore :
DA  - 2022.
DO  - 10.1007/978-981-16-3420-8
DO  - doi
ID  - 1444772
KW  - Machine learning.
KW  - Mathematical optimization.
KW  - Apprentissage automatique.
KW  - Optimisation mathématique.
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-3420-8
N2  - 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.
PB  - Springer,
PP  - Singapore :
PY  - 2022.
SN  - 9789811634208
SN  - 9811634203
T1  - Distributed machine learning and gradient optimization/
TI  - Distributed machine learning and gradient optimization/
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-3420-8
ER  -