Recurrent Neural Networks (RNNs) are a class of neural networks designed to handle sequential data, such as text, time series, or speech. Unlike traditional feedforward networks, RNNs have loops allowing information to persist across time steps.
📌 Key Features of RNNs
- Temporal Dependency: Process data in sequences, maintaining memory of previous inputs.
- Variable Length Input/Output: Handles tasks with dynamic input sizes (e.g., sentences of different lengths).
- Applications: Used in natural language processing, machine translation, and speech recognition.
🧩 RNN Architecture
The basic RNN cell consists of:
- Input Gate: Controls information flow into the cell.
- Hidden State: Stores context from previous steps.
- Output Gate: Determines what information to output.
⚠️ Note: RNNs can suffer from vanishing gradient issues, leading to the development of advanced variants like LSTM and GRU.
📚 Advanced RNN Variants
- Long Short-Term Memory (LSTM):
- Gated Recurrent Unit (GRU): Simplified version of LSTM with fewer parameters.
- Bidirectional RNN: Processes data in both forward and backward directions.
📝 Example Use Cases
- Text Generation:
- Chatbots: Understanding context in conversation sequences.
- Time Series Forecasting: Predicting future values based on historical data.
For deeper exploration, check our guide on Sequence-to-Sequence models to see how RNNs are extended for complex tasks!