Bayesian Optimization in Action

by Quan Nguyen

Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide.In Bayesian Optimization in Action you will learn how 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 cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch 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. Forewords by Luis Serrano and David Sweet. About the technology In machine learning, optimization is about achieving the best predictions—shortest delivery routes, perfect price points, most accurate recommendations—in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. About the book Bayesian Optimization in Action teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you’ll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You’ll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. What's inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the reader For machine learning practitioners who are confident in math and statistics. About the author Quan Nguyen is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. Table of Contents 1 Introduction to Bayesian optimization 2 Gaussian processes as distributions over functions 3 Customizing a Gaussian process with the mean and covariance functions 4 Refining the best result with improvement-based policies 5 Exploring the search space with bandit-style policies 6 Leveraging information theory with entropy-

423 pages, Kindle Edition

Published January 9, 2024

Reviews

[Nick · Rating 5 out of 5]

This is a great resource to learn about Bayesian Optimization.
The book features a lot of images and code that help the learning process.
The writing keeps a conversational tone. The mathematical is kept to a minimum but it doesn't lose depth.
I liked learning about different acquisition functions and also different heuristics, such as Bayesian Optimization policies, multi-armed bandit policies and entropy-based policies.

This book is suitable to data scientists and ML practitioners, particular those who have an interest in hyperparameter tuning, A/B testing, decision-making and optimization. It is also suited to researchers who face similar optimization problems in their domain.

[Mirerfan Gheibi · Rating 5 out of 5]

If you haven't explored BO yet, you should give it a read. This book starts with the basics, swiftly progressing to real-world examples that simplify complex concepts. It delves deep into the theory and practical application of Bayesian Optimization, making it accessible to both beginners and seasoned practitioners.