GRU (Gated Recurrent Unit)
A type of recurrent neural network that is similar to LSTM but with a simpler architecture.
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
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