Exploring the Concept of Recurrent Neural Networks

Abstract

Recurrent neural networks are a type of artificial neural network designed for pattern recognition from data sequences. Examples are time series and natural languages. This paper discusses the design, functioning, and applications of RNNs along with the advantages and disadvantages concerning the alternative architectures of neural networks. Furthermore, the paper delves deeper by exploring the developments in RNN models, such as the widely used Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), addressing the long-standing vanishing gradient issue with which traditional RNNs have been plagued. Finally, we would end with a discussion on the position of RNNs in the landscape of deep learning and possible future courses.