Hyperparameter Optimization

Hyperparameter optimization involves searching for the best configuration of model hyperparameters that cannot be learned directly from the training data. This includes parameters like learning rate, network architecture, and regularization strength. Various methods can be used, including grid search, random search, and Bayesian optimization.

Model tuning, Architecture search, Automated machine learning