Recurrent Neural Networks (RNN) are a class of artificial neural networks that are capable of learning from sequence data. They are particularly useful for tasks such as language modeling, speech recognition, and time series analysis.
Basic Concepts
- Neural Network: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
- Sequence Data: Data that is ordered in time or space, such as time series, text, or audio.
Why RNN?
Traditional neural networks are not well-suited for sequence data because they do not have the ability to remember information from previous steps in the sequence. RNNs, on the other hand, are designed to handle sequence data by maintaining a hidden state that captures information from previous steps.
Types of RNNs
- Simple RNN: The simplest form of RNN, where the hidden state is updated at each time step.
- LSTM (Long Short-Term Memory): A type of RNN that can learn long-term dependencies by using gates to control the flow of information.
- GRU (Gated Recurrent Unit): A variant of LSTM that is simpler and more efficient.
Applications
- Language Modeling: Predicting the next word in a sentence.
- Speech Recognition: Transcribing spoken words into text.
- Time Series Analysis: Forecasting future values based on past data.
Further Reading
For more information on RNNs, you can check out our Deep Learning Tutorial.