An Overview on the Artificial Neural Network

Jitender Kumar

Abstract

Deep learning is the cutting edge of artificial intelligence, which is already at the forefront (AI) (AI). Machine learning, on the other hand, is meant to teach computers how to interpret and learn from data. Deep learning enables a computer to continually educate itself to examine data, learn from it, and enhance its capabilities. This article gives a quick description of the Artificial Neural Network forecasting method (ANN) (ANN). It is used to boost the model's forecast accuracy while lowering the model's dependency on test data or current value. The fundamental developments in technology that have been applied in MATLAB are described, as well as distinct ANN discrete sets. The goal of the preparation is to keep the input equations' mean square errors to a minimal. The ANN model may be used to forecast yield boundaries, which assists in the best estimation of machining borders for the purpose of measuring improving streamlining machining boundaries.

Keywords

Artificial Neural Network, Algorithm, Deep learning, MATLAB.

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Cites this article as

J. Kumar "An Overview on the Artificial Neural Network", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-8, no.3, pp.274-278, 2020. Available from: https://doi.org/10.55524/ijircst.2020.8.3.43

Corresponding Author

Jitender Kumar

Assistant Professor, Department of Computer Science and Engineering, Vivekananda Global, University, Jaipur, India (Email Id- jitender_kumar@vgu.ac.in)

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