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, stock prices, and natural language text. Unlike feedforward neural networks, RNNs have loops, allowing information to persist, making them capable of learning from previous input data.
Key Concepts
- Sequential Data: RNNs are particularly useful for sequential data, where the order of the data points is important.
- Backpropagation Through Time (BPTT): This is the method used to train RNNs by propagating errors backwards through time.
- Long Short-Term Memory (LSTM): An RNN architecture that can learn long-term dependencies in data.
Example
Here's an example of how RNNs can be used to predict the next word in a sentence:
- Input: "I am going to the"
- Output: "store"
Resources
- Deep Learning with Python - A comprehensive guide to deep learning with Python.
- Natural Language Processing with Python - Learn about NLP and how to implement it using Python.
RNN Diagram
RNNs are a powerful tool for processing sequential data. By understanding the key concepts and learning from examples, you can start to implement and utilize RNNs in your projects.