Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research

Abstract

AbstractRecurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.