Externally indexed torrent

If you are the original uploader, contact staff to have it moved to your account

Textbook in PDF format

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.
Introduction to Bayesian optimization
Gaussian Processes
Gaussian processes as distributions over functions
Customizing a Gaussian process with the mean and covariance functions
Bayesian Optimization
Refining the best result with improvement-based policies
Exploring the search space with bandit-style policies
Leveraging information theory with entropy-based policies
Specialized Optimization Settings
Maximizing throughput with batch and asynchronous optimization
Satisfying extra constraints with constrained optimization
Balancing utility and cost with cost-aware optimization
Learning from pairwise comparisons with preference optimization
Optimizing multiple objectives at the same time
Special Gaussian Process Models
Scaling Gaussian processes to large data sets
Combining Gaussian processes and neural networks
Appendix: Solutions to exercises