Gaussian Mixture Models
Deep Learning
A simplified version of LSTM networks that combines the forget and input gates into a single update gate
Detailed Explanation
Gated Recurrent Units (GRUs) are a type of recurrent neural network designed to efficiently model sequential data. They simplify the LSTM architecture by combining the forget and input gates into one update gate, reducing complexity and computational cost. This enables faster training while maintaining effective memory retention, making GRUs suitable for various sequence modeling tasks such as speech recognition and language modeling.
Use Cases
- •GRUs are used to improve real-time speech recognition accuracy with faster, efficient sequence modeling.
Related Terms
Other terms in the Deep Learning category