Bayesian Optimization in Action MEAP V12

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

712 243 20MB

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Bayesian Optimization in Action MEAP V12

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Quan Nguyen

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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