An Introductory Review of Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) have made incredible progress in their usage with their different type to address a wide range of practical issues especially recently. Deep neural networks, for example, frequently contain incredibly complicated internal architecture, yet they also perform well and have great accuracy. In certain practical applications, this flaw renders neural networks as opaque as a black box, which is undesirable. Based on this contradictory issue, we first describe and summarize its composite and the mechanism by which the information circles in this network. Secondly, how can achieve great success in accurate prediction by building a model that can understand the data? After that, we review the various types of ANNs that are currently used. We also discuss existing issues and potential directions of ANN models.