Deep learning algorithms and their relevance: A review

Authors

DOI:

https://doi.org/10.59461/ijdiic.v2i4.78

Keywords:

Neural networks, Convolutional Neural network, Recurrent Neural Network, Long Short-Term Memory, Generative Adversarial Network

Abstract

Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. This paper discusses deep learning and various supervised, unsupervised, and reinforcement learning models. An overview of Artificial neural network(ANN), Convolutional neural network(CNN), Recurrent neural network (RNN), Long short-term memory(LSTM), Self-organizing maps(SOM), Restricted Boltzmann machine(RBM), Deep Belief Network (DBN), Generative adversarial network(GAN), autoencoders, long short-term memory(LSTM), Gated Recurrent Unit(GRU) and Bidirectional-LSTM is provided. Various deep-learning application areas are also discussed. The most trending Chat GPT, which can understand natural language and respond to needs in various ways, uses supervised and reinforcement learning techniques. Additionally, the limitations of deep learning are discussed. This paper provides a snapshot of deep learning.

Downloads

Published

10-11-2023

How to Cite

Nisha.C.M, & N. Thangarasu. (2023). Deep learning algorithms and their relevance: A review. International Journal of Data Informatics and Intelligent Computing, 2(4), 1–10. https://doi.org/10.59461/ijdiic.v2i4.78

Issue

Section

Regular Issue