The process compares configurations such as learning rates, batch sizes, regularization strengths, or architecture choices. Search may be manual, random, grid-based, or guided by an optimization algorithm, while test data remains separate.
Hyperparameter tuning searches for training settings that produce strong validation performance.
The process compares configurations such as learning rates, batch sizes, regularization strengths, or architecture choices. Search may be manual, random, grid-based, or guided by an optimization algorithm, while test data remains separate.