Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are capable of recognizing patterns in sequences of data such as text, genomes, and time series. Unlike feedforward neural networks, RNNs have loops allowing information to persist, making them well-suited for tasks involving temporal dependencies.

Key Characteristics of RNNs

  • Sequential Data: RNNs are designed to handle sequences of data, such as time series or text.
  • Memory: They have the ability to maintain a form of state, which allows them to use information from previous inputs.
  • Backpropagation Through Time (BPTT): RNNs use a variant of backpropagation called BPTT to train on sequences.

Types of RNNs

  • Simple RNN: The simplest form of RNN, where the output of the previous time step is fed back into the network.
  • Long Short-Term Memory (LSTM): A type of RNN that is capable of learning long-term dependencies, overcoming the vanishing gradient problem.
  • Gated Recurrent Unit (GRU): Similar to LSTM, but simpler and more efficient.

Applications of RNNs

  • Natural Language Processing (NLP): Language translation, sentiment analysis, and text generation.
  • Time Series Analysis: Stock market prediction, weather forecasting.
  • Speech Recognition: Transcribing spoken words into written text.

Recurrent Neural Network Diagram

For more in-depth information on RNNs, you can check out our Deep Learning tutorials.