Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are well-suited for sequence prediction problems. Unlike feedforward neural networks, RNNs have loops allowing information to persist, making them capable of learning from sequences of data.

Key Concepts

  • Input Sequence: The sequence of data points that the RNN processes.
  • Hidden State: The state of the RNN at any given time, which carries information from previous time steps.
  • Output Sequence: The sequence of predictions or decisions made by the RNN.

Types of RNNs

  • Basic RNN: The most straightforward type of RNN, which can learn simple sequence patterns.
  • Long Short-Term Memory (LSTM): A type of RNN designed to overcome the vanishing gradient problem, making it capable of learning long-term dependencies.
  • Gated Recurrent Unit (GRU): An alternative to LSTM, which is simpler and faster to train.

Applications

RNNs have a wide range of applications, including:

  • Natural Language Processing (NLP)
  • Speech Recognition
  • Time Series Analysis
  • Machine Translation

Resources

For further reading, you might want to check out our Introduction to RNNs.

LSTM Structure
GRU Structure