Recurrent Neural Network (RNN)
is a type of neural network designed to process sequential data by using information from previous inputs to influence the processing of current inputs.
What is Recurrent Neural Network (RNN)?
An RNN processes data one step at a time while maintaining a hidden state that carries information from earlier steps in the sequence. This allows the network to account for relationships between elements across a sequence, such as words in a sentence or values in a time series.
Why is Recurrent Neural Network (RNN) Important?
RNNs are useful for tasks where the order and context of data matter. They provide a foundation for sequence modeling and have also led to architectures such as LSTM and GRU that address some limitations of traditional RNNs.
Common use cases
RNNs are commonly used for language modeling, speech recognition, time-series forecasting, sequence classification, sentiment analysis, and other sequential data tasks.