A Review of Deep Learning Research

被引:130
作者
Mu, Ruihui [1 ,2 ]
Zeng, Xiaoqin [1 ]
机构
[1] Hohai Univ, Coll Comp & Informat, Nanjing 210098, Jiangsu, Peoples R China
[2] Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453000, Henan, Peoples R China
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2019年 / 13卷 / 04期
基金
中国国家自然科学基金;
关键词
Deep learning; machine learning; artificial intelligence; learning model; neural network; optimization method;
D O I
10.3837/tiis.2019.04.001
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.
引用
收藏
页码:1738 / 1764
页数:27
相关论文
共 54 条
[1]  
[Anonymous], 2012, Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT
[2]  
[Anonymous], 2012, LECT NOTES COMPUTER
[3]  
[Anonymous], 2011, International Conference on Artificial Intelligence and Statistics
[4]  
[Anonymous], IDC iView: IDC Analyze the future
[5]  
[Anonymous], 2014, ARXIV
[6]  
Bengio Y, 2001, ADV NEUR IN, V13, P932
[7]   Learning Deep Architectures for AI [J].
Bengio, Yoshua .
FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2009, 2 (01) :1-127
[8]   AUTO-ASSOCIATION BY MULTILAYER PERCEPTRONS AND SINGULAR VALUE DECOMPOSITION [J].
BOURLARD, H ;
KAMP, Y .
BIOLOGICAL CYBERNETICS, 1988, 59 (4-5) :291-294
[9]  
Cho K, 2014, ARXIV14061078
[10]  
Collobert R, 2011, J MACH LEARN RES, V12, P2493