Hyperparameter Optimization (Advanced Algorithms)

Advanced hyperparameter optimization moves beyond simple grid or random search to intelligently find the best configuration for a machine learning model's non-trainable parameters (e.g., learning rate, number of layers). Techniques like Bayesian optimization build a probabilistic model of the objective function to guide the search, while evolutionary algorithms or population-based training explore the space more broadly. These methods aim to reduce computational cost while finding better performing models.