The field of natural language understanding is a common area where recurrent neural networks (RNNs) tend to excel. You may imagine tasks such as recognizing named entities and classifying the predominant sentiment in a given piece of text. However, as we mentioned, RNNs are applicable to a broad spectrum of tasks that involve modeling time-dependent sequences of data. Generating music is also a sequence modeling task as we tend to distinguish music from a cacophony by modeling the sequence of notes that are played in a given tempo.
RNN architectures are even applicable for some visual intelligence tasks, such as video activity recognition. Recognizing whether a person is cooking, running, or robbing a bank in a given video is essentially modeling sequences of human movements and matching them to specific classes. In fact, RNNs have been deployed...
