Comparative Analysis of Deep Learning Techniques

Rutika Pawar

Abstract: Deep learning is a sub part of machine learning & artificial intelligence. Deep Learning is one of the rising focus areas in data science. With a tremendous rise of accessibility of data in recent years, it has become vital to develop models that will solve the new technology problems. Recently, deep learning has been utilized in varied recognitions and classifications model due to its high potential to deliver exceptional results. As compared to other learning algorithms, deep learning aims to solve real world problems with limited resources. This paper summarized the need of deep learning along with its differentiation factors with respect to machine learning algorithms. It has addresses a comparative study of four different neural networks used in deep learning techniques. A comparative table of algorithms is discuses in the end, that emphasizes the use case and specialty of particular algorithms. Overall, this paper serves as a smaller picture depicting all aspects of deep learning and a wider knowledge of horizon. It can be used for academic purposes and industry uses.

Keywords: Deep learning, machine learning, artificial intelligence, neural networks, hidden layers, perceptron

How to Cite?: Rutika Pawar, "Comparative Analysis of Deep Learning Techniques", Volume 13 Issue 1, January 2024, International Journal of Science and Research (IJSR), Pages: 1740-1745, https://www.ijsr.net/getabstract.php?paperid=SR24127165416, DOI: https://dx.doi.org/10.21275/SR24127165416