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Bayesian Optimization

64,19 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 18b63af1b3fd7c4f80b3fe87a923e9c9bf
Autor: Liu, Peng
Themengebiete: Bayesian optimization BoTorch Gaussian process Machine Learning Python hyper parameter tuning
Veröffentlichungsdatum: 24.03.2023
EAN: 9781484290620
Sprache: Englisch
Seitenzahl: 234
Produktart: Kartoniert / Broschiert
Verlag: APRESS
Untertitel: Theory and Practice Using Python
Produktinformationen "Bayesian Optimization"
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 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.What You Will LearnApply Bayesian Optimization to build better machine learning modelsUnderstand and research existing and new Bayesian Optimization techniquesLeverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner workingDig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimizationWho This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.

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