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Beschreibung
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 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 For
Beginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science. 64,19 €
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Bis zu 50 % günstiger als neu
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Technische Daten
Erscheinungsdatum
24.03.2023
Sprache
Englisch
EAN
9781484290620
Herausgeber
APRESS
Sonderedition
Nein
Autor
Peng Liu
Seitenanzahl
234
Auflage
1
Einbandart
Broschiert
Buch Untertitel
Theory and Practice Using Python
Schlagwörter
Python, Machine Learning, Bayesian optimization, hyper parameter tuning, Gaussian process, BoTorch
Thema-Inhalt
UYQM - Maschinelles Lernen
UMX - Programmier- und Skriptsprachen, allgemein
UYQ - Künstliche Intelligenz
Höhe
254 mm
Breite
17.8 cm
Hersteller: Apress, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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