Machine Learning & Training

What Is Hyperparameter Tuning?

Hyperparameter tuning is the process of selecting the configuration settings that control how a model learns, such as the learning rate or number of layers. Unlike model parameters, these are set before training and not learned from data. Effective tuning can significantly improve a model's performance.