Book

Hyperparameter Tuning with Python

This book curates numerous hyperparameter tuning methods for Python all in one place, providing a deep explanation of how each method works, and a decision map that can help you choose which hyperparameter tuning method is right for your specific problem and situation.

Offered by

Difficulty Level

Intermediate

Completion Time

10h12m approx.

Language

English

Certification

Not available

About Course

Book Content

chapters • 10h12m total length

1. Evaluating Machine Learning Models

2. Introducing Hyperparameter Tuning

3. Exploring Exhaustive Search

4. Exploring Bayesian Optimization

5. Exploring Heuristic Search

6. Exploring Multi-Fidelity Optimization

7. Hyperparameter Tuning via Scikit

8. Hyperparameter Tuning via Hyperopt

9. Hyperparameter Tuning via Optuna

10. Advanced Hyperparameter Tuning with DEAP and Microsoft NNI

11. Understanding Hyperparameters of Popular Algorithms

12. Introducing Hyperparameter Tuning Decision Map

13. Tracking Hyperparameter Tuning Experiments

14. Conclusions and Next Steps

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