Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. In this hands-on guide, Quan Nguyen shows how to put its advanced techniques into practice. The book shows how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques.

Author: Quan Nguyen
Publisher: Manning
Date: November 2023
Pages: 424
ISBN: 978-1633439078
Print: 1633439070
Kindle: B0CK8ZDMG5
Audience: developers interested in machine learning
Level: Intermediate/advanced
Category: Artificial Intelligence and Mathematics 

Topics include:

• 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

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