Learning Rate
Learning Rate is a fundamental tuning hyperparameter in gradient descent optimizers that determines the mathematical step size taken toward the global minimum of the loss function during model training.
Frequently Asked Questions
What happens if the learning rate is set too high?▼
The optimizer can overshoot the global minimum, causing the training loss to diverge or fluctuate wildly instead of converging.
What happens if the learning rate is set too low?▼
The parameter updates will be extremely small, making the training process slow and potentially getting stuck in local minima.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationGradient descent optimization, hyperparameter tuning runs, and neural training configuration.
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