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 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 .Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyIn 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 bookBayesian 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 insideAbout the readerFor machine learning practitioners who are confident in math and statistics.About the authorQuan 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 Contents1 Introduction to Bayesian optimizationPART 1 MODELING WITH GAUSSIAN PROCESSES2 Gaussian processes as distributions over functions3 Customizing a Gaussian process with the mean and covariance functionsPART 2 MAKING DECISIONS WITH BAYESIAN OPTIMIZATION4 Refining the best result with improvement-based policies5 Exploring the search space with bandit-style policies6 Leveraging information theory with entropy-based policiesPART 3 EXTENDING BAYESIAN OPTIMIZATION TO SPECIALIZED SETTINGS7 Maximizing throughput with batch optimization8 Satisfying extra constraints with constrained optimization9 Balancing utility and cost with multifidelity optimization10 Learning from pairwise comparisons with preference optimization11 Optimizing multiple objectives at the same timePART 4 SPECIAL GAUSSIAN PROCESS MODELS12 Scaling Gaussian processes to large datasets13 Combining Gaussian processes with neural networks