Example Concept: Hyperparameter optimization is the process of finding the best set of parameters for a neural network to improve its performance. This involves selecting values for hyperparameters such as learning rate, batch size, and architecture details (e.g., number of layers and units per layer). Techniques like grid search systematically explore a predefined set of hyperparameter values, while random search samples random combinations. More advanced methods like Bayesian optimization use probabilistic models to predict the performance of hyperparameter combinations, thus efficiently navigating the search space.