Example Concept: Hyperparameter optimization is the process of finding the best set of hyperparameters for a neural network by evaluating different combinations and selecting the one that yields the best performance on a validation set. Common methods include grid search, which exhaustively tries all combinations, random search, which samples random combinations, and Bayesian optimization, which uses probabilistic models to predict the performance of hyperparameter combinations and select the most promising ones.