Artículos relacionados a Distributed Machine Learning and Gradient Optimization...
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
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 a Professor at the School of EECS and Director of the Institute of Network Computing and Information Systems, at Peking University. His research interests include database system architectures, query and index techniques, and big data management and mining. He has published over 200 refereed papers at international conferences and in journals. Dr. Cui has served on the technical program committee of various international conferences, including SIGMOD, VLDB, ICDE and KDD, and as Vice PC Chair of ICDE 2011, Demo Co-Chair of ICDE 2014, Area Chair of VLDB 2014, PC Co-Chair of APWeb 2015 and WAIM 2016. He is currently a member of the trustee board of VLDB Endowment, is on the editorial board of the VLDB Journal, Distributed and Parallel Databases Journal, and Information Systems, and was formerly an associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE, 2009-2013). He was selected for a Microsoft Young Professorship award (MSRA 2008), CCF Young Scientist award (2009), Second Prize of Natural Science Award of MOE China (2014), and appointed a Cheung Kong distinguished Professor by the MOE in 2016.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 167,32
EUR 16,98 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 127,54
EUR 9,77 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoOtras ediciones populares con el mismo título
Resultados de la búsqueda para Distributed Machine Learning and Gradient Optimization...
Imagen de archivo
Distributed Machine Learning and Gradient Optimization (Big Data Management)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26388127495
Cantidad disponible: 1 disponibles
Imagen de archivo
Distributed Machine Learning and Gradient Optimization
Librería: Basi6 International, Irving, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Nº de ref. del artículo: ABEJUNE24-314915
Cantidad disponible: 1 disponibles
Imagen de archivo
Distributed Machine Learning and Gradient Optimization (Big Data Management)
Librería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 391505112
Cantidad disponible: 1 disponibles
Imagen de archivo
Distributed Machine Learning and Gradient Optimization (Big Data Management)
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 18388127501
Cantidad disponible: 1 disponibles
Imagen del vendedor
Distributed Machine Learning and Gradient Optimization
Publicado por
Springer, Berlin|Springer Nature Singapore|Springer, 2021
ISBN 10: 981163419X
ISBN 13: 9789811634192
Nuevo
Tapa dura
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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, imp. Nº de ref. del artículo: 473138653
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Distributed Machine Learning and Gradient Optimization (Big Data Management)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9789811634192_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Distributed Machine Learning and Gradient Optimization
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 44301030-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Distributed Machine Learning and Gradient Optimization
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 44301030-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Distributed Machine Learning and Gradient Optimization
Publicado por
Springer Nature Singapore Feb 2022, 2022
ISBN 10: 981163419X
ISBN 13: 9789811634192
Nuevo
Tapa dura
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 184 pp. Englisch. Nº de ref. del artículo: 9789811634192
Cantidad disponible: 2 disponibles
Imagen del vendedor
Distributed Machine Learning and Gradient Optimization
Publicado por
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 981163419X
ISBN 13: 9789811634192
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9789811634192
Cantidad disponible: 1 disponibles