Apply advanced techniques for optimizing machine learning processes. Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy.
Bayesian Optimization in Action teaches you how to build Bayesian optimization systems from the ground up. This book transforms state-of-the-art research into usable techniques that you can easily put into practice, all fully illustrated with useful code samples.
Hone your understanding of Bayesian optimization through engaging examples—from forecasting the weather, to finding the optimal amount of sugar for coffee, and even deciding if someone is psychic! Along the way, you’ll explore scenarios for when there are multiple objectives, when each decision has its own cost, and when feedback is in the form of pairwise comparisons. With this collection of techniques, you’ll be ready to find the optimal solution for everything from transport and logistics to cancer treatments.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author(s): Quan Nguyen
Publisher: Manning Publications
Year: 2023
Language: English
Pages: 593
MEAP_VERSION_12
2 Welcome
3 1_Introduction_to_Bayesian_optimization
4 2_Gaussian_processes_as_distributions_over_functions
5 3_Customizing_a_Gaussian_process_with_the_mean_and_covariance_functions
6 4_Refining_the_best_result_with_improvement-based_policies
7 5_Exploring_the_search_space_with_bandit-style_policies
8 6_Leveraging_information_theory_with_entropy-based_policies
9 7_Maximizing_throughput_with_batch_optimization
10 8_Satisfying_extra_constraints_with_constrained_optimization
11 9_Balancing_utility_and_cost_with_multi-fidelity_optimization
12 10_Learning_from_pairwise_comparisons_with_preference_optimization
13 11_Optimizing_multiple_objectives_at_the_same_time
14 12_Scaling_Gaussian_processes_to_large_datasets
15 13_Combining_Gaussian_processes_with_neural_networks
16 Appendix._Solutions_to_the_exercises
