Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	Februar 2022
Genre:	EDV / Informatik
	Data Mining / distributedmachinelearning / gradientcompression / gradientoptimization / Maschinelles Lernen / parallelism / synchronizationprotocol
ISBN:	9789811634192
EAN-Code:	9789811634192
Verlag:	Springer
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 241 mm / B 160 mm / D 16 mm
Gewicht:	448 gr
Seiten:	184
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.