Bayesian optimization: Theory and practice with Python
Publication Type
Book
Publication Date
3-2023
Abstract
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.
Keywords
Machine learning, Bayesian optimization
Discipline
Categorical Data Analysis | Finance and Financial Management
Research Areas
Quantitative Finance
First Page
1
Last Page
234
ISBN
9781484290620
Publisher
Apress
Citation
LIU, Peng.
Bayesian optimization: Theory and practice with Python. (2023). 1-234.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7201