Recurrent Neural Networks (RNNs) 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, such as sentences, time series, or stock prices.
Why RNNs?
RNNs are designed to handle sequence data, which makes them suitable for tasks where the order of data points is important. Unlike traditional neural networks, RNNs can maintain a "memory" of previous inputs, allowing them to capture temporal dependencies in the data.
Types of RNNs
- Simple RNN: The most basic form of RNN, which processes input sequences one by one.
- LSTM (Long Short-Term Memory): An advanced type of RNN that can learn long-term dependencies.
- GRU (Gated Recurrent Unit): Another advanced type of RNN that is simpler and more efficient than LSTM.
Example: Language Modeling
Language modeling is the task of predicting the next word in a sequence of words. RNNs are particularly well-suited for this task due to their ability to capture the temporal dependencies in language.
Resources
For more information on RNNs, you can check out our Introduction to Neural Networks tutorial.