Deep Learning Solicitation in Machine Vision System

Authors

  • Tanvi Khanna  Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management (Formerly Northern India Engineering College) Affiliated to Guru Gobind Singh Indraprastha University, New Delhi, India
  • Saakshi Agrawal  Assistant Professor, Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management (Formerly Northern India Engineering College) Affiliated to Guru Gobind Singh Indraprastha University, New Delhi, India

DOI:

https://doi.org//10.32628/CSEIT206255

Keywords:

Artificial Intelligence, Deep Learning, Machine Learning, Activation function, Applications of Deep Learning in various fields, Deep Learning Benefits.

Abstract

Artificial intelligence is the science that entirely focuses on creating intelligent machines, softwares that can think, mimic and responds like humans. Deep Learning is subspace of Machine Learning. [2] In Deep Learning, the systems are equipped for learning and separating the information that is unstructured or unlabeled. Deep Leaning is a component of Artificial Intelligence that mimics the activity of human mind in preparing information and making designs for use in decision making. It is otherwise called known as deep neural learning or deep neural system. Deep Learning offers various applications in real world areas. This paper describes about the deep learning evolution and the methodology involved in it. The role of activation function in deep learning is described in this paper along with the future aspects of deep learning application in real world.

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Published

2020-04-30

Issue

Section

Research Articles

How to Cite

[1]
Tanvi Khanna, Saakshi Agrawal, " Deep Learning Solicitation in Machine Vision System, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.230-235, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT206255