Bayesian Optimization : Theory and Practice Using Python
Liu, Peng
Sold by GreatBookPrices, Columbia, MD, U.S.A.
AbeBooks Seller since April 6, 2009
New - Soft cover
Condition: New
Quantity: Over 20 available
Add to basketSold by GreatBookPrices, Columbia, MD, U.S.A.
AbeBooks Seller since April 6, 2009
Condition: New
Quantity: Over 20 available
Add to basketThis 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 completingthis 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.
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