What is Hyperparameter Tuning?
Hyperparameter Tuning is the process of selecting the optimal values for hyperparameters in a
machine learning model. Hyperparameters are parameters that are not learned from the data but set manually before training. Examples include learning rate, regularization strength, and network architecture.
Hyperparameter tuning is typically done through methods such as grid search, random search, or more advanced techniques like
Bayesian optimization.
