Deep Learning: Concepts, Architectures, Workflow, Applications and Future Directions

Abstract

In recent years, deep learning (DL) has evolved into a statistical method in machine learning (ML) that produces surprising results in complex tasks, similar to human performance. Deep learning technology, derived from artificial neural networks (ANN), is a major advance in computer science because it allows learning from data. The ability to learn large amounts of data is one of the benefits of deep learning. In recent years, the field of higher education has grown rapidly and has been successfully used in many cultural fields. Deep learning outperforms well-known machine learning methods in a variety of fields, including cyber security, natural language processing, bioinformatics, robotics, and medical data tracking and management. To provide a better starting point from which to develop a comprehensive understanding of deep learning, this article also aims to take a closer look at even the most important aspects of deep learning, including current developments. In addition, this article highlights the importance of deep learning and explores different deep learning methods and networks.