Paper Info
Title
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
Abstract
The 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 development 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 communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial 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.
Year
DOI
Venue
2019
10.1007/s10462-018-09679-z
Artificial Intelligence Review
Keywords
Field
DocType
Machine Learning, Deep Learning, Large-scale data mining, Artificial Intelligence software, Parallel processing, Intensive computing, Graphics processing unit (GPU)
Data mining,Computer science,Massively parallel,CUDA,Artificial intelligence software,Implementation,Software,Artificial intelligence,Deep learning,Big data,Machine learning,Software development
Journal
Volume
Issue
ISSN
52
1
0269-2821
Citations 
PageRank 
References 
15
0.82
31
Authors
8
Name
Order
Citations
PageRank
Giang T. Nguyen16716.69
Stefan Dlugolinsky2549.32
Martin Bobák3163.59
Viet D. Tran46515.84
Álvaro López García5497.19
Ignacio Heredia6151.16
Peter Malík7244.25
Ladislav Hluchý821240.92