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  3. Machine learning

Bayesian Optimization

- Theory and Practice Using Python
Af: Peng Liu Engelsk Paperback

Bayesian Optimization

- Theory and Practice Using Python
Af: Peng Liu Engelsk Paperback
Tjek vores konkurrenters priser

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 For
Beginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.
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Tjek vores konkurrenters priser
Normalpris
kr 573
Fragt: 39 kr
6 - 8 hverdage
20 kr
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God 4 anmeldelser på
Tjek vores konkurrenters priser

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 For
Beginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 234
ISBN-13: 9781484290620
Indbinding: Paperback
Udgave:
ISBN-10: 1484290623
Kategori: Machine learning
Udg. Dato: 24 mar 2023
Længde: 0mm
Bredde: 178mm
Højde: 254mm
Forlag: APress
Oplagsdato: 24 mar 2023
Forfatter(e): Peng Liu
Forfatter(e) Peng Liu

Kategori Machine learning

ISBN-13 9781484290620

Sprog Engelsk

Indbinding Paperback

Sider 234

Udgave

Længde 0mm

Bredde 178mm

Højde 254mm

Udg. Dato 24 mar 2023

Oplagsdato 24 mar 2023

Forlag APress

Kategori sammenhænge