Treffer: Review of Graph-Powered Machine Learning, Alessandro Negro, Manning Publication 2020
Title:
Review of Graph-Powered Machine Learning, Alessandro Negro, Manning Publication 2020
Authors:
Contributors:
WEB Architecture x Semantic WEB x WEB of Data (WEB3), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Publisher Information:
CCSD, 2020.
Publication Year:
2020
Collection:
collection:CNRS
collection:LIRMM
collection:OPENDATA
collection:MIPS
collection:UNIV-MONTPELLIER
collection:FADO
collection:UM-2015-2021
collection:WEB-CUBE
collection:LIRMM
collection:OPENDATA
collection:MIPS
collection:UNIV-MONTPELLIER
collection:FADO
collection:UM-2015-2021
collection:WEB-CUBE
Subject Terms:
Original Identifier:
HAL: hal-03046388
Document Type:
Zeitschrift
article<br />Journal articles
Language:
English
Availability:
Accession Number:
edshal.hal.03046388v1
Database:
HAL
Weitere Informationen
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.