What are RNNs?
Recurrent Neural Networks 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:
- Time Series Prediction 📈
- Natural Language Processing 📘
- Speech Recognition 🎤
They are often visualized as networks with chains of nodes, where each node represents a time step. 💡
Key Components
1. Hidden State
The hidden state captures information about previous inputs. It's updated using:
hidden_state = tanh(W_hh * hidden_state + W_xh * input)
2. Weights
W_hh: Connections between hidden statesW_xh: Connections between inputs and hidden statesW_ho: Connections between hidden states and outputs
3. Activation Functions
Commonly uses tanh or sigmoid functions to introduce non-linearity. 📊
Applications
- Machine Translation 🌍 (e.g., translating English to French)
- Sentiment Analysis 😊/😢
- Chatbots 💬
- Music Generation 🎵
For deeper insights into sequence modeling, check our Sequence Modeling Tutorial. 📘