Learning Rate Decay
Machine Learning
A hyperparameter that controls how much to adjust the model in response to the estimated error each time the model weights are updated.
Detailed Explanation
The learning rate is a crucial hyperparameter in machine learning that determines the size of the steps taken during optimization. It controls how quickly the model adapts to the estimated error by updating weights. A small learning rate results in slow learning, while a large one can cause overshooting and instability. Proper tuning ensures efficient convergence to optimal results.
Use Cases
- •Adjusting the learning rate improves model training speed and stability during neural network optimization.
Related Terms
Other terms in the Machine Learning category