Smarter Artificial Intelligence with Deep Learning

International Journal of Computer Science and Engineering
© 2018 by SSRG - IJCSE Journal
Volume 5 Issue 6
Year of Publication : 2018
Authors : Dr.V.V.Narendra Kumar, T.Satish Kumar

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How to Cite?

Dr.V.V.Narendra Kumar, T.Satish Kumar, "Smarter Artificial Intelligence with Deep Learning," SSRG International Journal of Computer Science and Engineering , vol. 5,  no. 6, pp. 10-16, 2018. Crossref, https://doi.org/10.14445/23488387/IJCSE-V5I6P102

Abstract:

The unpredictable growth in large-scale computing capabilities, availability of large datasets, and advancements in learning techniques etc. made it necessary for Deep Learning. The rapid growth in the mentioned areasresulted in varied deep learning frameworks. But there are several inefficiencies in these frameworks in user and developer point of view.Moreover, adopting useful techniques across frameworks in performing learning tasks and optimizingperformance has become very essential. Deep learning (DL) is a set of diversified approacheswhere machine learning can be innovative and to helping computers to usebig data i.e., huge amounts of data which is in the form of text, images and sound. Deep networks can be trained with vast amounts of data using deep learning algorithms. High level abstractions in data can be modelled using deep learning based on a set of algorithms. It is a new research area where Machine Learning can be drawn nearer to Artificial Intelligence. DL is used in various fields for achieving multiple levels of abstraction like sound, text, images feature extraction etc. Deep Learning is used by popular search engines like Google in its voice and image recognition algorithms, and by Netflix and e-commerce websites like Amazon, to decide what consumer wants to buy next, and even by researchers at MIT in predicting the future. Hence Deep Learning gained much significance in recent days. Many Universities started various courses in Deep Learning which indicates the importance of Deep Learning in the academic world.

Keywords:

  Deep learning, deep machine learning, Supervised Learning, Artificial Intelligence, Artificial Neural Networks

References:

[1]. Matthew N. O. Sadiku, Mahamadou Tembely, and Sarhan M. Musa, “Deep learning,” International Research Journal of Advanced Engineering and Science, Volume 2, Issue 1, pp. 77-78, 2017. 
[2]. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. 
[3]. L. Arnold et al., “An introduction to deep learning,” Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 477-488, April 2011. 
[4]. W. Wang et al., “Deep learning at scale and at ease,” ACM Transactions on Computer Communication Applications, vol. 12, no. 4s, pp. 69:1- 69:25, Nov. 2016. 
[5]. Arel, I., Rose, D. C., &Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research [research frontier].Computational Intelligence Magazine, IEEE, 5(4), 13-18. 
[6]. Schmidhuber, Jurgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015): 85-117. 
[7]. SSSN Usha Devi N,Dr R. Mohan,Dr P.KiranSree, “An Extensive Survey on Deep Learning Applications”, International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Volume 6, Issue 2 February 2017, 81-84. 
[8]. Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, et al. Deep learning for content-based image retrieval: a comprehensive study. In: ACM MM; 2014. p. 157–66.