Example Concept: Hyperparameter tuning involves systematically adjusting parameters like learning rate, batch size, and number of layers to improve model performance. Techniques such as grid search, random search, and Bayesian optimization are commonly used to explore the hyperparameter space efficiently. Cross-validation is often employed to validate the model's performance across different subsets of data, ensuring robustness and preventing overfitting.