A Survey Study of the Deep Learning for Convolutional Neural Network Architecture

Abstract

The deep learning (DL) computer paradigm has been the industry standard for machine learning (ML) during the past few years. It has gradually become the most widely used computational technique in machine learning. One of the benefits of DL is its ability to learn massive amounts of data. Deep learning has seen tremendous growth in the last several years and has been successfully used for many traditional applications. More importantly, DL has outperformed popular machine learning algorithms in several domains, such as cybersecurity, bioinformatics, robotics, etc. The field remains mostly uneducated although it has been contributed to several works reviewing the State-ofthe- Art on DL, each of which only covered a specific aspect of the field. We thus propose a more holistic approach to this contribution, providing a more suitable basis upon which to construct a thorough understanding of DL. Concerning the most significant DL features, including the most recent advancements in the field, this evaluation specifically aims to provide a more thorough survey. This study specifically describes the kinds of DL networks and techniques, as well as their significance. The most common type of DL network, convolutional neural networks (CNNs), is then presented, and the evolution of it.