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.
<ASIN:1633439070>
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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