Hyperparameter Tuning with Python. Boost your machine learning model’s performance via hyperparameter tuning Rydułtowy

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 …

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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 its important. Next, youll 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. Spis treści: 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 O autorze: Louis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects. Finally, Louis loves to meet new friends! So, please feel free to reach out to him on LinkedIn if you have any topics to be discussed.

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Podstawowe informacje

Autor
  • Louis Owen
Wydawnictwo
  • Packt Publishing
Format
  • PDF
  • EPUB
Ilość stron
  • 306
Rok wydania
  • 2022