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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning

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Bayesian Optimization - Theory and Practice Using Python (Paperback, 1st ed.) Loot Price: R1,182
Discovery Miles 11 820
You Save: R336 (22%)
Bayesian Optimization - Theory and Practice Using Python (Paperback, 1st ed.): Peng Liu

Bayesian Optimization - Theory and Practice Using Python (Paperback, 1st ed.)

Peng Liu

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List price R1,518 Loot Price R1,182 Discovery Miles 11 820 | Repayment Terms: R111 pm x 12* You Save R336 (22%)

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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.

General

Imprint: Apress
Country of origin: United States
Release date: May 2023
Firstpublished: 2023
Authors: Peng Liu
Dimensions: 254 x 178mm (L x W)
Format: Paperback
Pages: 234
Edition: 1st ed.
ISBN-13: 978-1-4842-9062-0
Categories: Books > Computing & IT > Computer programming > Programming languages > General
Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN: 1-4842-9062-3
Barcode: 9781484290620

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