Bayesian Optimization: Theory and Practice Using Python

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
  

Who This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.

本書以直觀且豐富的圖解方式，涵蓋了流行的貝葉斯優化技術的基本理論和實現方法。本書介紹的技術將使您能夠更好地調整機器學習模型的超參數，並學習到全局優化的節約樣本方法。

本書首先介紹了不同的貝葉斯優化（BO）技術，包括常用工具和高級主題。它使用Python逐步從頭開始開發，逐漸發展到更高級的庫，如最近由Facebook推出的開源項目BoTorch。在此過程中，您將看到這一重要學科的實際實現，以及對基本理論的全面覆蓋和簡單明瞭的解釋。本書旨在彌合研究人員和從業人員之間的差距，為兩者提供全面、易於理解和有用的參考指南。

閱讀完本書後，您將對貝葉斯優化技術有牢固的掌握，並能夠在自己的機器學習模型中實踐。

您將學到什麼：
- 應用貝葉斯優化來構建更好的機器學習模型
- 理解和研究現有和新的貝葉斯優化技術
- 利用高性能庫，如BoTorch，使您能夠深入研究和編輯內部運作
- 深入研究在貝葉斯優化中引導搜索過程的常用優化算法的內部運作

本書適合對機器學習、分析或其他與數據科學相關的初級到中級專業人士。

Peng Liu is an assistant professor of quantitative finance (practice) at Singapore Management University and an adjunct researcher at the National University of Singapore. He holds a Ph.D. in statistics from the National University of Singapore and has ten years of working experience as a data scientist across the banking, technology, and hospitality industries

Peng Liu 是新加坡管理大學的量化金融（實務）助理教授，也是新加坡國立大學的兼職研究員。他擁有新加坡國立大學的統計學博士學位，並在銀行、科技和酒店業等行業擁有十年的數據科學家工作經驗。