Proceedings of the 5th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2019)

Overview of Deep Learning Research

Authors
Yanmei Liu, Yuda Chen
Corresponding Author
Yanmei Liu
Available Online December 2019.
DOI
10.2991/sschd-19.2019.124How to use a DOI?
Keywords
Deep learning, Neural network, Network structure, Deep learning framework.
Abstract

Deep learning is the research hotspot and direction in all major fields at present, its motivation lies in the establishment and simulation neural network of human brain's analysis and learning, and to interpret data by imitating the mechanism of human brain. This paper mainly overviewed the related research of deep learning. Firstly, it introduced its concept, and introduced several important neural network models in detail, compared and analyzed the current mainstream deep learning framework, finally introduced the main application field, and prospected the future research directions.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 5th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2019)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
December 2019
ISBN
10.2991/sschd-19.2019.124
ISSN
2352-5398
DOI
10.2991/sschd-19.2019.124How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yanmei Liu
AU  - Yuda Chen
PY  - 2019/12
DA  - 2019/12
TI  - Overview of Deep Learning Research
BT  - Proceedings of the 5th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2019)
PB  - Atlantis Press
SP  - 615
EP  - 621
SN  - 2352-5398
UR  - https://doi.org/10.2991/sschd-19.2019.124
DO  - 10.2991/sschd-19.2019.124
ID  - Liu2019/12
ER  -