Example Concept: Hyperparameter optimization involves selecting the best set of parameters that define the model structure and training process, such as learning rate, batch size, and number of layers. Efficient methods like Bayesian optimization use probabilistic models to predict the performance of different hyperparameter combinations and iteratively refine these predictions to find the optimal set with fewer evaluations compared to exhaustive search methods.