A Recurrent Neural Network (RNN) is a type of artificial neural network used to process sequential or temporal data. Unlike feedforward neural networks, in which information flows in one direction only, in RNNs information flows in a loop, i.e. the output at one point in time is used as input at the next point in time.
The ability to process data streams makes them useful for a wide variety of applications, such as speech recognition, machine translation and text generation. One of the main features of RNNs is their ability to model long-term dependencies in data streams.
RNNs have a recurrent structure, which allows information to flow from one layer to another through a hidden state, which stores information about previous states. The hidden state is updated at each time step and is used to influence the output at the next time step. This allows the RNN to have long-term memory and be able to capture patterns in data sequences that extend over time.
Natural Language Processing or NLP analyzes how machines understand, interpret and process human language.
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