Description

Gated Recurrent Unit (GRU) is a type of recurrent neural network architecture that is similar to Long Short-Term Memory (LSTM) but with a simpler design. GRUs use update and reset gates to solve the vanishing gradient problem of traditional RNNs. This allows them to effectively capture long-term dependencies in sequential data. GRUs are often computationally more efficient than LSTMs while achieving comparable performance on many tasks.

Examples

• 📊 Time series prediction
• 🗣️ Speech recognition
• 📝 Text classification

Applications