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 strats for hyperparameter tuning
Navigate a search space and identify high-perfog 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.