GLOSSARY > HYPERPARAMETER TUNING
Hyperparameter Tuning
The process of determining the right combination of hyperparameters that allows the machine learning model to maximize its performance.
Unlike model weights which are learned during training, hyperparameters (like learning rate, batch size, and network depth) are set beforehand. Hyperparameter Tuning involves computationally expensive grid searches to find the exact configuration that yields the most accurate and efficient Private AI model.
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