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

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