Bayesian Optimization in Action (MEAP V7)

Apply advanced techniques for optimizing machine learning processes. Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. In Bayesian Optimization in Action you will learn how to 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 strats for hyperparameter tuning Navigate a search space and identify high-perfog regions Apply Bayesian optimization to practical use cases such as cost-constrained, multi-objective, and preference optimization Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimization Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn't have to be difficult! You'll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting edge Python libraries. The book's easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. MEAP_VERSION_7 Welcome 1_Introduction_to_Bayesian_optimization 2_Gaussian_processes_as_distributions_over_functions 3_Customizing_a_Gaussian_process_with_the_mean_and_covariance_functions 4_Refining_the_best_result_with_improvement-based_policies 5_Exploring_the_search_space_with_bandit-style_policies 6_Leveraging_information_theory_with_entropy-based_policies 7_Maximizing_throughput_with_batch_optimization 8_Satisfying_extra_constraints_with_constrained_optimization Appendix_A._Solutions_to_the_exercises