Name: Bayesian Optimization. Theory and Practice Using Python (en Inglés)
Price: 63650.00 CLP
Availability: InStock
Author: Peng Liu
ISBN: 9781484290644

portada Bayesian Optimization. Theory and Practice Using Python (en Inglés)

Bayesian Optimization. Theory and Practice Using Python (en Inglés)

Formato

Libro Físico

Editorial

Apress

Autor

Peng Liu

Año

2023

Idioma

Inglés

N° páginas

252

Encuadernación

Tapa Blanda

Dimensiones

25.40 x 17.80 x 1.30 cm

ISBN13

9781484290644

Bayesian Optimization. Theory and Practice Using Python (en Inglés)

Peng Liu (Autor) · Apress · Tapa Blanda

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Reseña del libro "Bayesian Optimization. Theory and Practice Using Python (en Inglés)"

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 optimization

Who This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.