Bayesian Optimization in Action (Paperback)

Apply advanced techniques for optimizing machine learning processes. Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy.

In Bayesian Optimization in Action you will learn how to:

• Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to practical use cases such as cost-constrained, multi-objective, and preference optimization Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimization

Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn't have to be difficult! You'll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting edge Python libraries. The book's easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian optimization helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian optimization can help you make better decisions, faster.

About the book

Bayesian Optimization in Action teaches you how to build Bayesian optimization systems from the ground up. This book transforms state-of-the-art research into usable techniques that you can easily put into practice, all fully illustrated with useful code samples. In it, you'll hone your understanding of Bayesian optimization through engaging examples--from forecasting the weather, to finding the optimal amount of sugar for coffee, and even deciding if someone is psychic! Along the way, you'll explore scenarios for when there are multiple objectives, when each decision has its own cost, and when feedback is in the form of pairwise comparisons. With this collection of techniques, you'll be ready to find the optimal solution for everything from transport and logistics to cancer treatments.

About the reader

For machine learning practitioners who are confident in math and statistics.

About the author

Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems that involve uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a Ph.D. degree in computer science at Washington University in St. Louis where he does research on Bayesian methods in machine learning.

應用先進技術優化機器學習過程。貝葉斯優化能夠以速度和準確性找出機器學習模型的最佳配置。

在《貝葉斯優化實戰》中，您將學習以下內容：
- 在稀疏和大型數據集上訓練高斯過程
- 將高斯過程與深度神經網絡結合，使其具有靈活性和表達能力
- 尋找超參數調整的最成功策略
- 導航搜索空間並識別高性能區域
- 將貝葉斯優化應用於成本限制、多目標和偏好優化等實際案例
- 使用PyTorch、GPyTorch和BoTorch實現貝葉斯優化

《貝葉斯優化實戰》向您展示如何應用尖端的貝葉斯技術優化超參數調整、A/B測試和機器學習過程的其他方面。本書使用清晰的語言、插圖和具體的示例，證明貝葉斯優化並不難！您將深入了解貝葉斯優化的工作原理，並學習如何使用最先進的Python庫實現它。本書易於重用的代碼示例讓您可以直接將其應用於自己的項目中。

購買印刷版書籍還包括Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。

關於技術：
在科學和工程領域進行實驗可能既昂貴又耗時，尤其在沒有可靠的方法縮小選擇範圍的情況下。貝葉斯優化可以幫助您在搜索空間中確定最佳配置。它使用高斯過程和機器學習技術對目標函數進行建模並量化預測的不確定性。無論您是調整機器學習模型、向客戶推薦產品還是從事研究，貝葉斯優化都可以幫助您更快地做出更好的決策。

關於本書：
《貝葉斯優化實戰》教您如何從頭開始構建貝葉斯優化系統。本書將最先進的研究轉化為可用的技術，並提供了豐富的代碼示例。通過引人入勝的示例，您將深入了解貝葉斯優化，從預測天氣到找到最佳咖啡糖量，甚至判斷某人是否具有超能力！在此過程中，您將探索多目標情況、每個決策的成本以及以成對比較形式提供反饋的情境。憑藉這些技術，您將能夠找到從運輸和物流到癌症治療的最佳解決方案。

關於讀者：
適合具備數學和統計知識的機器學習從業人員。

關於作者：
Quan Nguyen是一位Python程序員和機器學習愛好者。他對解決涉及不確定性的決策問題感興趣。Quan已經撰寫了幾本關於Python編程和科學計算的書籍。他目前在華盛頓大學攻讀計算機科學博士學位，並在機器學習中的貝葉斯方法方面進行研究。

Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems that involve uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a PhD degree in Computer Science at Washington University in St. Louis, where he conducts research on Bayesian methods in machine learning.

Quan Nguyen 是一位 Python 程式設計師和機器學習愛好者。他對解決涉及不確定性的決策問題感興趣。Quan 在 Python 程式設計和科學計算方面撰寫了幾本書籍。他目前正在華盛頓大學聖路易斯分校攻讀計算機科學博士學位，並在那裡進行關於機器學習中的貝葉斯方法的研究。