Recurrent Neural Networks are used for learning sequential information. These sequential problems consist of cycles that make use of the underlying time-steps. In order to compute this data, ANNs require a separate memory cell in order to store the data of the previous step.
We use data that is represented in a series of time-steps. This makes RNN an ideal algorithm for solving problems related to text processing.
In the context of text-processing, RNNs are useful for predicting future sequences of words. RNNs that are stacked altogether are referred to as Deep Recurrent Neural Networks. RNNs are used in generating text, composing music and for time-series forecasting.
Chatbots, recommendation systems and speech recognition systems use varying architectures of Recurrent Neural Networks.