Recurrent neural networks, abbreviated as RNNs, is used in cases of sequential data, whether as an input, output, or both. The reason RNNs became so effective is because of their architecture to aggregate the learning from the past datasets and use that along with the new data to enhance the learning. This way, it captures the sequence of events, which wasn't possible in a feed forward neural network nor in earlier approaches of statistical time series analysis.
Consider time series data such as stock market, audio, or video datasets, where the sequence of events matters a lot. Thus, in this case, apart from the collective learning from the whole data, the order of learning from the data encountered over time matters. ...
