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 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 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

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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

PART 1 MODELING WITH GAUSSIAN PROCESSES

2 Gaussian processes as distributions over functions

3 Customizing a Gaussian process with the mean and covariance functions

PART 2 MAKING DECISIONS WITH BAYESIAN OPTIMIZATION

4 Refining the best result with improvement-based policies

5 Exploring the search space with bandit-style policies

6 Leveraging information theory with entropy-based policies

PART 3 EXTENDING BAYESIAN OPTIMIZATION TO SPECIALIZED SETTINGS

7 Maximizing throughput with batch optimization

8 Satisfying extra constraints with constrained optimization

9 Balancing utility and cost with multifidelity optimization

10 Learning from pairwise comparisons with preference optimization

11 Optimizing multiple objectives at the same time

PART 4 SPECIAL GAUSSIAN PROCESS MODELS

12 Scaling Gaussian processes to large datasets

13 Combining Gaussian processes with neural networks

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