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.
Keywords: Convolution neural network, Deep learning applications, Deep Learning, Image classification, Machine Learning, Medical image analysis.Natural Language Processing.
