Comparing Bayesian Optimization with Other Optimization Methods

Understand the strengths and limitations of Bayesian optimization compared to other optimization methods such as gradient descent, random search, evolutionary algorithms, and grid search. Learn how Bayesian optimization's global scope, efficiency, and model-based approach make it suitable for complex, high-dimensional optimization problems and hyperparameter tuning in machine learning.