Overview
GRUs are similar to LSTMs but have a simpler structure, making them computationally more efficient and faster to train while often achieving similar performance.
Key Differences from LSTM
- Fewer Gates: GRUs have two gates (Update and Reset) instead of three.
- No Cell State: GRUs only use a hidden state to pass information.
When to use GRU
GRUs are often preferred for smaller datasets or when computational resources are limited, as they have fewer parameters to learn.