An Overview of Machine Learning, Deep Learning and Neural Networks

Authors

  • Pranav Murali  SRM University, Electronics and Communication, Chennai, India

Keywords:

Algorithms, regression, computation, networks, neurons, training, data

Abstract

Every year millions of data are being generated by people around the world and they have to be processed quickly and efficiently. Mistakes could be made by humans but a computer or a system with a function to perform ,will never. So , it is essential for us to help the computer around us to do tasks that we humans take much more time to implement. This paper covers the picture of the much spoken about machine learning, deep learning and neural networks. At the end of this paper , we will have an understanding of these technologies and a good grasp of the process behind it.

References

  1. Jensen, F. V. (1996). An introduction to Bayesian networks 
  2. Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques
  3. R Raina A Madhavan AY Ng. Large-scale deep unsupervised learning using graphics processorsJ].
  4. Logistic Regression http://www.learnbymarketing.com/methods/logistic-regression-explained/
  5. Nearest Neighbours http://www.statsoft.com/Textbook/k-Nearest-Neighbors
  6. Data science comparison with big data , https://www.simplilearn.com/data-science-vs-bigdata-vs-data-analytics-article
  7. E-learning industry, https://elearningindustry.com/bigdata-in- elearning-future-of-elearning-industry

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Pranav Murali, " An Overview of Machine Learning, Deep Learning and Neural Networks, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.1590-1595, March-April-2018.