A comprehensive classification of deep learning libraries

Conference paper

Pandey, H. and Windridge, D. 2019. A comprehensive classification of deep learning libraries. Yang, X.S., Sherratt, S., Dey, N. and Joshi, A. (ed.) 3rd International Congress on Information and Communication Technology. London, UK 27 - 28 Feb 2018 Singapore Springer. pp. 427-435 https://doi.org/10.1007/978-981-13-1165-9_40
TypeConference paper
TitleA comprehensive classification of deep learning libraries
AuthorsPandey, H. and Windridge, D.
Abstract

Deep Learning (DL) networks are composed of multiple processing layers that learn data representations with multiple levels of abstraction. In recent years, DL networks have significantly improved the state-of-the-art across different domains, including speech processing, text mining, pattern recognition, object detection, robotics and big data analytics. Generally, a researcher or practitioner who is planning to use DL networks for the first time faces difficulties in selecting suitable software tools. The present article provides a comprehensive list and taxonomy of current programming languages and software tools that can be utilized for implementation of DL networks. The motivation of this article is hence to create awareness among researchers, especially beginners, regarding the various languages and interfaces that are available to implement deep learning, and to provide a simplified ontological basis for selecting between them.

KeywordsDeep learning; Deep learning libraries; Machine learning ; Deep belief network
Research GroupArtificial Intelligence group
Conference3rd International Congress on Information and Communication Technology
Page range427-435
Proceedings TitleThird International Congress on Information and Communication Technology: ICICT 2018, London
SeriesAdvances in Intelligent Systems and Computing
EditorsYang, X.S., Sherratt, S., Dey, N. and Joshi, A.
ISSN2194-5357
Electronic2194-5365
ISBN
Hardcover9789811311642
Electronic9789811311659
PublisherSpringer
Place of publicationSingapore
Publication dates
Online29 Sep 2018
Print2019
Publication process dates
Deposited03 Jul 2018
Completed27 Feb 2018
Accepted18 Dec 2017
Output statusPublished
Accepted author manuscript
File Access Level
Open
Copyright Statement

This is a pre-copyedited version of a contribution published in Third International Congress on Information and Communication Technology: ICICT 2018, London, Editors: Yang, X-S., Sherratt, S., Dey, N., Joshi, A. (2019) published by Springer Nature. The definitive authenticated version is available online via https://doi.org/10.1007/978-981-13-1165-9_40

Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-13-1165-9_40
Scopus EID2-s2.0-85054313934
Web of Science identifierWOS:000455759800040
LanguageEnglish
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Windridge, D. and Bober, M. 2014. A kernel-based framework for medical big-data analytics. in: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges Springer. pp. 197-208
High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)
Abbey, C., Hope, C., Sterr, A., Elangovan, P., Geades, N., Windridge, D., Young, K., Wells, K. and Mello-Thoms, C. 2013. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP). SPIE Proceedings Vol. 8673. https://doi.org/10.1117/12.2007557
Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene
Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060
Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535