Peng Liu - Bayesian Optimization [2023, PDF/EPUB, ENG] torrent

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Bayesian Optimization

Год издания: 2023
Автор: Peng Liu

Издательство: Apress
ISBN: 978-1-4842-9063-7
Язык: Английский

Формат: PDF/ePub
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 243

Описание: This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.

The book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics. It follows a “develop from scratch” method using Python, and gradually builds up to more advanced libraries such as BoTorch, an open-source project introduced by Facebook recently. Along the way, you’ll see practical implementations of this important discipline along with thorough coverage and straightforward explanations of essential theories. This book intends to bridge the gap between researchers and practitioners, providing both with a comprehensive, easy-to-digest, and useful reference guide.
After completing this book, you will have a firm grasp of Bayesian optimization techniques, which you’ll be able to put into practice in your own machine learning models.

What You Will Learn
- Apply Bayesian Optimization to build better machine learning models
- Understand and research existing and new Bayesian Optimization techniques
- Leverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner working
- Dig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimization
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About the Author ix
About the Technical Reviewer xi
Acknowledgments xiii
Introduction xv
Chapter 1: Bayesian Optimization Overview 1
Chapter 2: Gaussian Processes 33
Chapter 3: Bayesian Decision Theory and Expected Improvement 69
Chapter 4: Gaussian Process Regression with GPyTorch 101
Chapter 5: Monte Carlo Acquisition Function with Sobol Sequences and Random Restart 131
Chapter 6: Knowledge Gradient: Nested Optimization vs. One-Shot Learning 155
Chapter 7: Case Study: Tuning CNN Learning Rate with BoTorch 185
Index 225
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