Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to handle sequential data. Unlike traditional feedforward networks, RNNs have loops allowing information to persist. This makes them ideal for tasks like language modeling, speech recognition, and time series prediction.
Key Features of RNNs
- Memory Capability: RNNs use hidden states to retain information from previous steps in a sequence.
- Variable-Length Input/Output: They can process inputs and generate outputs of different lengths.
- Versatile Applications: Used in natural language processing, machine translation, and more.
Common Use Cases
- 🗣️ Speech Recognition: Converting spoken language into text.
- 📖 Text Generation: Creating coherent text sequences (e.g., chatbots).
- 📈 Time Series Forecasting: Predicting future values based on historical data.
How RNNs Work
RNNs process data sequentially, updating hidden states at each step. For example, when analyzing a sentence:
- Input: Word 1 → Update hidden state
- Input: Word 2 → Use updated hidden state to process next word
- ...
- Output: Final prediction based on entire sequence
Further Reading
For a deeper dive into RNNs and their implementations, check out our RNN Tutorial. Explore advanced topics like LSTM or GRU cells by visiting /rnn_advanced.
Visual Examples
- 🧠 Neural Network Diagram: View here
- 📊 Sequence Prediction Visualization: Explore this
RNNs form the foundation of many modern NLP models. For hands-on practice, try the RNN Lab to build your own sequence models!