Bayesian Optimization in Action MEAP V12

Apply advanced techniques for optimizing machine learning processes. Bayesian optimization helps pinpoint the best confi

1,025 136 20MB

English Pages 593 Year 2023

Table of contents :
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

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