RNNs have several use cases. Here is a list of the most frequently used:
- Language modelling and text generation: This is the attempt to predict the likelihood of the next word, given a sequence of words. This is useful for language translation: the most likely sentence would be the one that is correct.
- Machine translation: This is the attempt to translate text from one language to another.
- Anomaly detection in time series: It has been demonstrated that LSTM networks in particular are useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long-term memory. For this reason they are useful for anomaly or fault detection in time series. Practical use cases are in log analysis and sensor data analysis.
- Speech recognition: This is the attempt to predict phonetic segments based on input sound waves and then to formulate...
