Overview of Deep Learning: Applications in Various Areas

Authors

  • Atul Kumar Assistant Professor, Department of Computer Application, Rajiv Gandhi Memorial Government College, Joginder Nagar, District Mandi, Himachal Pradesh, India

Keywords:

Deep learning, Convolutional neural networks, Recurrent neural networks.

Abstract

Deep learning is emerging field in the sense of artificial intelligence. Deep-learning innovation powers numerous parts of current society: from web quests to content sifting on interpersonal organizations to proposals on web-based business sites, and it is progressively present in shopper items, for example, cameras and cell phones. Deep learning offers effective procedures to discover designs in information for taking care of testing prescient issues. Deep learning is widely used now in various applications like speech recognition, pattern recognition and image processing. This paper discusses the overview of Deep learning, its various applications and various techniques used in deep learning. 

Cite this Article

Atul Kumar. Overview of Deep Learning: Applications in Various Areas. Journal of Artificial Intelligence Research & Advances 2018; 5(3): 39–48p.

References

Faizan Shaikh. Deep Learning Applications a beginner can build in minutes (using Python) 2017. https://www.

analyticsvidhya.com/blog/2017/02/6-deep-learning applications-beginner-python/.

Najafabadi MM, Villanustre F, Khoshgoftaar TM, Naeem S, Randall W and Edin M. Deep learning applications and challenges in big data analytics. Journal of Big Data. Feb 2015,2. 10.1186/s40537-014-0007-7.

Ciodaro T, Deva D, Seixas J et al. Online particle detection with neural networks based on topological calorimetry information. J. Phys. Conf. Series 368, in 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011), 5-9 September 2011, Uxbridge, London UK.

Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing. 2014; 3.

Tiny. Deep Learning applications .2014.https://en.wikipe2014.dia.org/wiki/Deep_learning.

Schmidhuber J. Deep learning in neural networks: An overview. Technical Report IDSIA03–14. Oct 2014; 222–231p.

Bengio Y. Learning Deep Architectures for AI., Foundations and Trends in Machine Learning , 2009; 2(1), 1-55p.10.1561/2200000006.

Recurrent Neural Network. . In Wikipedia. Retreived June 8, 2018, from https://en.wikipedia.org/wiki/Recurrent_neural_network.

Dong Yu, Li Deng. Deep learning and its applications to signal and information processing. IEEE Signal Processing Magazine. Jan 2011; 28(1): 145–154p.

Arel I, Rose DC, Karnowski TP. Deep machine learning: A new frontier in artificial intelligence research. IEEE Computational Intelligence Magazine, 5(4), Nov 2010; 13–18p.

Mamoshina P, Vieira A, Putin E et al. Applications of deep learning in biomedicine. Mol. Pharmaceutics. 2016; 13(5): 1445–54p.

Tompson J, Jain A, LeCun Y et al. Joint training of a convolutional network and a graphical model for human pose estimation. In Proc. Advances in Neural Information Processing Systems. Oct 2014; 1799–1807p.

Naveen Kumar. AgriAi- Deep learning In Agriculture 2017. http://businessworld.in/article/AgriAi Deep-Learning-In-Agriculture/08–09–2017–125699.

Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps. International Journal of Robotics Research. Mar 2015; 34(4–5): 705–724p.

Balci B, Saadati D, Shiferaw D. Handwritten text recognition using deep learning. CS231n: Convolutional Neural Networks for Visual Recognition. Stanford University, Course Project Report. Spring 2017.

Arel I, Rose DC, Karnowski TP. Deep machine learning – A new frontier in artificial intelligence research. IEEE Computational Intelligence Magazine. Nov 2010; 5(4): 13–18p.

Paul SS, Singh L. A review on advances in deep learning. In Proc. IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions. Apr 2015; 1–6p.

Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press.http://www.deeplearningbook.org/contents/intro.html.2016

Ren S, He K, Girshick R et al. Faster R-CNN: Towards real-time object detection with region proposal networks. Neural Information Processing Systems (NIPS). 2015.

Downloads

Published

2019-02-06

Issue

Vol. 5 No. 3 (2018)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.