Advantages:
The report not only introduces the application background and mathematical principles of Bayesian optimization but also applies and demonstrates the libraries involved in Python. The visualized results allow readers to intuitively perceive the impact of different parameters on iterations and delve into the internal logic of Bayesian algorithms. Finally, the report applies Bayesian optimization to hyperparameter tuning, comparing it with random search and drawing conclusions.
Shortcomings:
In fact, I couldn't find any shortcomings. This is a very comprehensive and detailed explanatory report, providing me with a comprehensive and profound understanding of Bayesian optimization.
Advantages: The report not only introduces the application background and mathematical principles of Bayesian optimization but also applies and demonstrates the libraries involved in Python. The visualized results allow readers to intuitively perceive the impact of different parameters on iterations and delve into the internal logic of Bayesian algorithms. Finally, the report applies Bayesian optimization to hyperparameter tuning, comparing it with random search and drawing conclusions.
Shortcomings: In fact, I couldn't find any shortcomings. This is a very comprehensive and detailed explanatory report, providing me with a comprehensive and profound understanding of Bayesian optimization.
Grading: 100%