Hyperparameter tuning involves systematically optimizing parameters like learning rate, batch size, and number of layers to improve model performance. Common approaches include grid search, random search, and Bayesian optimization.
Grid search exhaustively tests all combinations of predefined hyperparameter values, while random search samples random combinations. Though simpler, random search is often more efficient when only a few hyperparameters significantly impact performance. Bayesian optimization uses probabilistic models to guide the search, offering a more intelligent approach to tuning.
Automated tools like Optuna or Hyperopt simplify hyperparameter tuning, while frameworks like TensorFlow and PyTorch provide built-in support for experimentation. Cross-validation ensures robustness in evaluating tuned models.