A Survey on Deep Learning Techniques, Applications and Challenges
AUTHORS
Zhigang Zhang,Department of Computer Science & Engineering, Harbin University of Science and Technology, China
ABSTRACT
Deep learning is a developing examination region in machine learning and example acknowledgment field. Profound learning alludes to machine learning procedures that utilization administered or unsupervised techniques to naturally learn various leveled portrayals in profound structures for grouping. The goal is to find more extract includes in the more elevated amounts of the portrayal, by utilizing neural systems which effectively isolates the different informative factors in the information. In the ongoing years it has pulled in much consideration because of its cutting edge execution in various territories like question discernment, discourse acknowledgment, PC vision, community oriented separating and regular dialect handling. As the information continues getting greater, profound learning is coming to assume a key part in giving enormous information prescient examination arrangements. This paper exhibits a short outline of profound learning, procedures, ebb and flow look into endeavors and the difficulties associated with it.
KEYWORDS
Auto-Encoders, Convolutional Neural Networks, Deep learning, RBM.
REFERENCES
[1] V.Pream Sudha et al., A survey on deep learning techniques, applications and challenges, International Journal of Advance Research In Science And Engineering, vol.4, no.3, pp.311-317. (2015)
[2] Marvin minsky, Seymour Papert, “Perceptrons : An introduction to computational geometry”, (1969)(CrossRef)(Google Scholar)
[3] Hinton, G. E., Osindero, S. and Teh, Y., “A fast learning algorithm for deep belief nets”, Neural Computation vol.18,Issue 7, pp1527-1554, (2006). https://doi.org/10.1162/neco.2006.18.7.1527(CrossRef)(Google Scholar)
[4] Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle, “Greedy Layer-Wise Training of Deep Networks”, in J. Platt et al. Advances in Neural Information Processing Systems 19 (NIPS 2006), pp.153-160, MIT Press, (2007)
[5] Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra and Yann LeCun “Efficient Learning of
[6] Sparse Representations with an Energy-Based Model”, in J. Platt et al. (Eds), Advances in Neural Information Processing Systems (NIPS 2006), MIT Press, (2007).
[7] Li Deng, Xiaodong He, Jianfeng Gao, “Deep stacking networks for information retrieval”, Acoustics, speech and signal processing, 2013 IEEE International conference.(2013) 10.1109/ICASSP.2013.6638239(CrossRef)(Google Scholar)
[8] Hua, Wang, M. A. Cuiqin, and Zhou Lijuan. “A brief review of machine learning and its application.” Information Engineering and Computer Science, ICIECS 2009. International Conference on. IEEE, (2009). 10.1109/ICIECS.2009.5362936(CrossRef)(Google Scholar)
[9] Zheng, Jiang, and Aldo Dagnino. "An initial study of predictive machine learning analytics on large volumes of historical data for power system applications." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, (2014) 10.1109/BigData.2014.7004327(CrossRef)(Google Scholar)
[10] Huang, He Helen, and Hang Liu. "Big data machine learning and graph analytics: Current state and future challenges." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, (2014) 10.1109/BigData.2014.7004471(CrossRef)(Google Scholar)
[11] Xu, Kaihe, et al. "Privacy-Preserving Machine Learning Algorithms for Big Data Systems." Distributed Computing Systems (ICDCS), 2015 IEEE 35th International Conference on. IEEE, (2015) 10.1109/ICDCS.2015.40(CrossRef)(Google Scholar)
[12] Baldominos, Alejandro, et al. "A scalable machine learning online service for big data real-time analysis." Computational Intelligence in Big Data (CIBD), 2014 IEEE Symposium on. IEEE, (2014) 10.1109/CIBD.2014.7011537
[13] Lai, Chih-Chin, and Ming-Chi Tsai. "An empirical performance comparison of machine learning methods for spam e-mail categorization." Hybrid Intelligent Systems, 2004. HIS'04. Fourth International Conference on. IEEE, (2004). 10.1109/ICHIS.2004.21(CrossRef)(Google Scholar)
[14] Singh, Jainendra. "Real time BIG data analytic: Security concern and challenges with Machine Learning algorithm." IT in Business, Industry and Government (CSIBIG), 2014 Conference on. IEEE, (2014) 10.1109/CSIBIG.2014.7056985(CrossRef)(Google Scholar)
[15] Navdeep Singh J, Survey of Machine learning applications, International Journal of Innovative Research in Computer and Communication Engineering, vol.5, no.8, pp.13871-13878.(2017)
CITATION
COPYRIGHT
© 2018 Zhang Zhigang. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.