Bayesian Optimization: Theory and Practice Using Python

This book provides a comprehensive and intuitive introduction to Bayesian optimization techniques, covering essential theory and implementation with Python. It bridges the gap between researchers and practitioners, offering a practical reference guide for building better machine learning models.

Format: Paperback / softback
Length: 234 pages
Publication date: 24 March 2023
Publisher: APress

This comprehensive book delves into the essential theory and practical implementation of popular Bayesian optimization techniques, offering a clear and intuitive approach for both researchers and practitioners. By employing a "develop from scratch" methodology using Python, readers will gain a deep understanding of the subject matter, gradually progressing to more advanced libraries like BoTorch. Throughout the journey, practical implementations and thorough explanations of key theories are provided, ensuring a solid foundation in this important discipline.

The book's primary objective is to bridge the gap between researchers and practitioners, providing a comprehensive and user-friendly reference guide that covers a wide range of topics. Whether you are a beginner or an intermediate-level professional in machine learning, analytics, or related data science roles, this book will empower you to apply Bayesian optimization to build better machine learning models and explore innovative optimization techniques.

In summary, this book is an invaluable resource for anyone seeking to enhance their understanding and expertise in Bayesian optimization, enabling them to optimize machine learning models and achieve optimal performance in their data science endeavors.

Weight: 485g
Dimension: 254 x 178 (mm)
ISBN-13: 9781484290620
Edition number: 1st ed.