Example Concept: Hyperparameter optimization is the process of tuning parameters that govern the training process of a neural network, such as learning rate, batch size, and number of layers. Techniques like grid search involve exhaustively searching through a predefined set of hyperparameter combinations, while random search samples random combinations. Bayesian optimization uses probabilistic models to predict the performance of hyperparameter settings and iteratively refines the search based on past results.