Breaks down the unique model structure of RNN’s that allows them to process sequential data by tracing the flow of data through the neural network. Highlights the wide range of applications for RNN’s, with examples cited from current scientific journals. Concludes with a summary of the weaknesses of RNN’s and how these may affect practitioners’ choices when using them.