Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements.
You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter.
By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.

Osoby które kupowały "Hyperparameter Tuning with Python", wybierały także:

• Windows Media Center. Domowe centrum rozrywki 66,67 zÅ‚, (8,00 zÅ‚ -88%)
• Ruby on Rails. Ćwiczenia 18,75 zÅ‚, (3,00 zÅ‚ -84%)
• Przywództwo w Å›wiecie VUCA. Jak być skutecznym liderem w niepewnym Å›rodowisku 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Scrum. O zwinnym zarzÄ…dzaniu projektami. Wydanie II rozszerzone 58,64 zÅ‚, (12,90 zÅ‚ -78%)
• Od hierarchii do turkusu, czyli jak zarzÄ…dzać w XXI wieku 58,64 zÅ‚, (12,90 zÅ‚ -78%)

Spis treści

Hyperparameter Tuning with Python. Boost your machine learning model’s performance via hyperparameter tuning eBook -- spis treÅ›ci

• 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