Recurrent Neural Networks (RNNs) add memory to neural networks by allowing information to flow in loops, enabling them to process sequences and remember past information.
The Revolutionary Idea:
• Feedback loops - outputs feed back as inputs
• Hidden state - network memory that persists
• Parameter sharing - same weights used at each time step
• Sequential processing - one step at a time
🧠 The Memory Breakthrough
RNNs are the first neural networks with memory! They can remember what they've seen before and use that information to understand new inputs.
Key Breakthroughs
🧠
Memory & Context
Networks can remember and build context over time
📏
Variable Length
Handle sequences of any length with same network
🔄
Parameter Sharing
Same weights applied at each time step - efficient scaling
⏰
Temporal Patterns
Learn patterns that evolve over time
✨
Generation
Create new sequences, not just classify existing ones
The RNN Architecture
→
↻
RNN Cell
→
Input → RNN (with Memory) → Output
The ↻ arrow shows the recurrent connection - output feeds back as input!
Traditional Neural Network
Input → Network → Output No feedback, no memory
Recurrent Neural Network
Input + Memory → Network → Output + New Memory Feedback creates memory and context