Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are designed to recognize patterns in sequences of data, such as time series or natural language. Unlike feedforward neural networks, RNNs have loops allowing information to persist, making them well-suited for tasks involving sequential data.
Key Features of RNNs
- Sequential Data: RNNs are designed to handle sequential data, where the output depends on the previous input.
- Feedback Loops: The loops in RNNs allow information to persist, enabling them to remember previous inputs.
- Backpropagation Through Time (BPTT): RNNs use BPTT for training, which involves backpropagating the error through the time steps.
Types of RNNs
- Simple RNN: The simplest form of RNN, where the hidden state is updated using the previous hidden state and the current input.
- Long Short-Term Memory (LSTM): LSTMs are a type of RNN designed to avoid the vanishing gradient problem and are well-suited for tasks involving long sequences.
- Gated Recurrent Units (GRU): GRUs are another type of RNN that are similar to LSTMs but have fewer parameters and are easier to train.
Applications of RNNs
- Natural Language Processing (NLP): RNNs are widely used in NLP tasks such as language translation, sentiment analysis, and text generation.
- Time Series Analysis: RNNs can be used for tasks such as stock price prediction and weather forecasting.
- Speech Recognition: RNNs are used in speech recognition systems to convert spoken words into text.
Resources
For more information on RNNs, you can visit our Deep Learning tutorials page.
Images
Recurrent Neural Network Architecture
Long Short-Term Memory (LSTM) Cell