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
Language
English
About Book
Who Is This Book For?
This book is for data scientists and ML engineers who are working with Python and want to further boost their ML model’s performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.
Hyperparameter Tuning with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 10h12m total length
Evaluating Machine Learning Models
Introducing Hyperparameter Tuning
Exploring Exhaustive Search
Exploring Bayesian Optimization
Exploring Heuristic Search
Exploring Multi-Fidelity Optimization
Hyperparameter Tuning via Scikit
Hyperparameter Tuning via Hyperopt
Hyperparameter Tuning via Optuna
Advanced Hyperparameter Tuning with DEAP and Microsoft NNI
Understanding Hyperparameters of Popular Algorithms
Introducing Hyperparameter Tuning Decision Map
Tracking Hyperparameter Tuning Experiments
Conclusions and Next Steps
Related Resources
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