Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are well-suited for sequence prediction problems. Unlike traditional feedforward neural networks, RNNs have loops allowing information to persist, making them ideal for tasks such as language modeling, speech recognition, and time series analysis.

Key Concepts

  • Input Sequence: The sequence of inputs that the RNN processes.
  • Hidden State: The state of the RNN at each time step, which holds information about the sequence processed so far.
  • Output Sequence: The sequence of outputs produced by the RNN.

Types of RNNs

  • Simple RNN: The simplest form of RNN, where the output at each time step depends only on the current input and the previous hidden state.
  • Long Short-Term Memory (LSTM): A type of RNN that can learn long-term dependencies by using a special kind of memory cell.
  • Gated Recurrent Unit (GRU): An alternative to LSTM, which is simpler and more efficient.

Example

Here's an example of how RNNs can be used for language modeling:

  • Input Sequence: "The quick brown fox jumps over the lazy dog"
  • Output Sequence: "The quick brown fox jumps over the lazy dog, and it was a sunny day."

Further Reading

For more information on RNNs, check out the following resources:

RNN Diagram