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 Characteristics of RNNs
- Time Dependency: RNNs process sequences of data over time, which allows them to remember previous inputs.
- Backpropagation Through Time (BPTT): A method for training RNNs, which involves propagating errors backwards through time.
- Vanishing Gradient Problem: A common issue in training RNNs, where gradients become very small or very large, making learning difficult.
Basic Structure of RNN
An RNN consists of a single layer with neurons connected in a loop. Each neuron takes as input the current input and the output of the previous neuron.
Types of RNNs
- Simple RNN: The most basic form of RNN, where the output of the previous neuron is fed back as input.
- Long Short-Term Memory (LSTM): A type of RNN that can learn long-term dependencies by using gates to control the flow of information.
- Gated Recurrent Unit (GRU): Similar to LSTM, but with a simpler structure and fewer parameters.
Applications of RNNs
RNNs have been successfully applied to various tasks, including:
- Language Modeling: Predicting the next word in a sentence.
- Machine Translation: Translating text from one language to another.
- Speech Recognition: Converting spoken language into text.
For more information on RNN applications, you can visit our Applications of RNN page.