Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/173822
COMPARTIR / EXPORTAR:
logo share SHARE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey

AutorNguyen, Giang; Dlugolinsky, Stefan; Bobak, Martin; Tran, Viet; López García, Álvaro CSIC ORCID ; Heredia, Ignacio CSIC ORCID; Malik, Peter; Hluchy, Ladislav
Fecha de publicación2019
EditorSpringer Nature
CitaciónArtificial Intelligence Review 52(1): 77-124 (2019)
ResumenThe combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software devel- opment in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source commu- nities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Arti- ficial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.
Versión del editorhttps://doi.org/10.1007/s10462-018-09679-z
URIhttp://hdl.handle.net/10261/173822
ISSN0269-2821
E-ISSN1573-7462
Aparece en las colecciones: (IFCA) Artículos




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
machinesurvey.pdf1 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

Page view(s)

600
checked on 26-mar-2024

Download(s)

1.285
checked on 26-mar-2024

Google ScholarTM

Check


Este item está licenciado bajo una Licencia Creative Commons Creative Commons