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Bayesian Optimization Theory Practice by Liu Peng (14 results)
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Condition: good. Has a sturdy binding with some shelf wear. May have some markings or highlighting. Used copies may not include access codes or Cd's. Slight bending may be present.
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Condition: New.
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Paperback or Softback. Condition: New. Bayesian Optimization: Theory and Practice Using Python. Book.
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Condition: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
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Condition: As New. Unread book in perfect condition.
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Bayesian Optimization : Theory and Practice Using Python
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Bayesian Optimization : Theory and Practice Using Python
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Paperback. Condition: Brand New. 249 pages. 10.00x7.01x0.53 inches. In Stock.
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Taschenbuch. Condition: Neu. Bayesian Optimization | Theory and Practice Using Python | Peng Liu | Taschenbuch | xv | Englisch | 2023 | Apress | EAN 9781484290620 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
