Recurrent Neural Network (RNN)

Executive Definition: Recurrent Neural Network (RNN) — A class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes.

Unlike standard feedforward neural networks, RNNs maintain a hidden state that acts as an internal memory, allowing them to process sequences of varying lengths. This makes them inherently suited for sequential data like text, speech, or time series. However, their sequential processing nature limits computational efficiency and makes learning long-range dependencies challenging.

Frequently Asked Questions

Why were RNNs largely superseded by Transformers in natural language processing?

RNNs process data sequentially, making parallelization difficult and training slow. They also struggle with long-term dependencies due to the vanishing gradient problem. Transformers solve both issues.

Are RNNs still used?

Yes. They are still utilized in applications where sequences are strictly time-dependent and relatively short, such as certain time series forecasting tasks or lightweight embedded applications.

What is an LSTM network?

Long Short-Term Memory (LSTM) is an advanced RNN architecture designed to overcome the vanishing gradient problem by using gating mechanisms to control the flow of information over long sequences.