What is an RNN?
A Recurrent Neural Network (RNN) is a type of artificial neural network designed to handle sequential data. Unlike traditional feedforward networks, RNNs have loops allowing information to persist across time steps. This makes them ideal for tasks like:
- Time series prediction
- Natural Language Processing (NLP)
- Speech recognition
- Sequence modeling
🧠 Key Feature: Memory through hidden states that carry information from previous steps to the current one.
RNN Architecture
Here's a breakdown of RNN components:
1. Input Layer
- Accepts sequential data (e.g., text, audio)
- Each time step processes one element of the sequence
2. Hidden Layer
- Contains neurons with recurrent connections
- Maintains a "memory" of previous inputs using hidden states
3. Output Layer
- Produces predictions at each time step
- Can be used for classification or regression
4. Weights
- Shared across all time steps for efficiency
- Crucial for learning temporal dependencies
How RNN Works
Time Step Processing
At each step, the network calculates:hidden_state = tanh(W_h * [previous_hidden_state, current_input] + b_h)output = W_o * hidden_state + b_oUnrolling the Network
Visualizing the sequence as a chain of computations (see diagram below):
- Backpropagation Through Time (BPTT)
Training method that adjusts weights by propagating errors backward across time steps.
Applications of RNN
- 📖 Language Modeling: Predicting the next word in a sentence
- 📊 Stock Price Forecasting: Analyzing time-dependent financial data
- 🎵 Music Generation: Creating sequences of musical notes
- 🌍 Machine Translation: Processing sequential input-output pairs
Practice Resources
Want to dive deeper? Check out these materials:
Tips for Success
✅ Start with simple sequences (e.g., character-level text)
✅ Use LSTM/GRU variants for better long-term memory
✅ Experiment with different architectures to find what works best
For visual learners, explore this sequence modeling example to see RNNs in action!