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Analysis on Deep Learning and its Types

Lakshmi Praba A.*, Preethi S. R. **

*-** Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamilnadu, India.

Periodicity:October - December'2020
DOI : https://doi.org/10.26634/jse.15.2.18164

Abstract

This paper focuses on compiling necessary aspects of deep learning for future researches. Deep learning has gained massive popularity in computing research and application development. Many cognitive problems related to unstructured problems are solved using this technology. Deep learning and Artificial intelligence are the underlying paradigms on popular applications like Google Translator. Machine learning and Deep learning are the two subset of Artificial intelligence. Deep learning algorithms are integrated using different neural network layers replicating the functioning of the human brain. Deep learning uses layers of neural networks to learn data in a recursive manner from training data from structured datasets and uses this data to predict unstructured data. Deep learning has three layers, namely input layer, output layer and hidden layer. Neural networks are often usual for the image recognition. Big Data powered with Deep learning can drive innovations beyond imagination in future.

Keywords

Deep Learning, Neural Network, Neurons, Artificial Intelligence, Main Layers.

How to Cite this Article?

Praba, A. L., and Preethi, S. R. (2020). Analysis on Deep Learning and its Types. i-manager's Journal on Software Engineering, 15(2), 25-30. https://doi.org/10.26634/jse.15.2.18164

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Analysis on Deep Learning and its Types

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