Ulteriori informazioni
Bayesian Optimization in Action teaches you how to build Bayesian Optimisation 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.
You will hone your understanding of Bayesian Optimisation 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 will explore scenarios with 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 will be ready to find the optimal solution for everything -- from transport and logistics to cancer treatments.
About the reader For machine learning practitioners who are confident in math and statistics.
Info autore
Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems that involve uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a PhD degree in Computer Science at Washington University in St. Louis, where he conducts research on Bayesian methods in machine learning.
Riassunto
Bayesian Optimization in Action teaches you how to build Bayesian Optimisation systems from the ground up. This book transforms state-of-the-art research into usable techniques you can easily put into practice. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!
