Distributed Machine Learning and Gradient Optimization / / by Jiawei Jiang, Bin Cui, Ce Zhang

• Record Nr.: UNINA9910743351903321
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
• ISBN: 981-16-3419-X; 981-16-3420-3
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (179 pages)
• Collana: Big Data Management, , 2522-0187
• Disciplina: 943.005
• Soggetti: Machine learning; Data mining; Database management; Machine Learning; Data Mining and Knowledge Discovery; Database Management
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 1 Introduction -- 2 Basics of Distributed Machine Learning -- 3 Distributed Gradient Optimization Algorithms -- 4 Distributed Machine Learning Systems -- 5 Conclusion. .
• Sommario/riassunto: 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.