Deep Learning: History and Evolution

Abstract

Recently, deep learning (DL) computing has become more popular in the machine learning (ML) community. In the field of ML, the most widely used computational approach is DL. It can solve many complex problems, cognitive tasks, and matching problems without any human performance or interface. ML cannot handle large amounts of data and DL can easily handle it. In the last few years, the field of DL has witnessed success in a range of applications. DL outperformed in many application domains, e.g., robotics, bioinformatics, agriculture, cybersecurity, natural language processing (NLP), medical information processing, etc. Despite various reviews on the state of the art in DL, they all concentrated on a single aspect of it, resulting in a general lack of understanding. There is a need to provide a better beginning point for comprehending DL. This paper aims to provide a more comprehensive overview of DL, including current advancements. This paper discusses the importance of DL and introduces DL approaches and networks. It then explains convolutional neural networks (CNNs), the most widely used DL network type and subsequent evolved model starting with LeNET, AlexNet with the Letnet-5, AlexNet, GoogleNet, and ResNet networks, and ending with the High-Resolution network. This paper also discusses the difficulties and solutions to help researchers recognize research gaps for DL applications.