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Bayesian Optimization - Theory and Practice Using Python (Paperback, 1st ed.)
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Bayesian Optimization - Theory and Practice Using Python (Paperback, 1st ed.)
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This book covers the essential theory and implementation of popular
Bayesian optimization techniques in an intuitive and
well-illustrated manner. The techniques covered in this book will
enable you to better tune the hyperparemeters of your machine
learning models and learn sample-efficient approaches to global
optimization. The book begins by introducing different Bayesian
Optimization (BO) techniques, covering both commonly used tools and
advanced topics. It follows a “develop from scratch” method
using Python, and gradually builds up to more advanced libraries
such as BoTorch, an open-source project introduced by Facebook
recently. Along the way, you’ll see practical implementations of
this important discipline along with thorough coverage and
straightforward explanations of essential theories. This book
intends to bridge the gap between researchers and practitioners,
providing both with a comprehensive, easy-to-digest, and useful
reference guide. After completing this book, you will have a firm
grasp of Bayesian optimization techniques, which you’ll be able
to put into practice in your own machine learning models. What You
Will Learn Apply Bayesian Optimization to build better machine
learning models Understand and research existing and new Bayesian
Optimization techniques Leverage high-performance libraries such as
BoTorch, which offer you the ability to dig into and edit the inner
working Dig into the inner workings of common optimization
algorithms used to guide the search process in Bayesian
optimization Who This Book Is ForBeginner to intermediate level
professionals in machine learning, analytics or other roles
relevant in data science.
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