Ilya Sutskever

Hyperparameter tuning involves adjusting the settings that control the learning process of a machine learning model, such as learning rate, number of layers, or regularization parameters. Proper tuning improves model performance by finding the optimal combination of hyperparameters through methods like grid search, random search, or Bayesian optimization, ultimately enhancing accuracy, efficiency, and generalization to unseen data.