More than 100 000 Accesses: Hyperparameter Tuning for Machine and Deep Learning

The Open Access Book „Hyperparameter Tuning for Machine and Deep Learning with R –
A Practical Guide“ has been accessed more than 100k times. It is freely available from Springer. Here is the download link: https://link.springer.com/book/10.1007/978-981-19-5170-1.

The book was published in 2023 as a practical guide for the statistical programming language R. Unfortunately, we are not able to maintain the R version any more. An archived version of the R software can be downloaded here: https://cran.r-project.org/src/contrib/Archive/SPOT/

An updated version of the book, which will use Python, is in preparation. See, e.g., PyTorch Hyperparameter Tuning – A Tutorial for spotPython on arXiv. The software will be available from GitHub as the Python package spotpython. Note: The package is still under development.

Overview

Editors:

• Eva Bartz, 
• Thomas Bartz-Beielstein, 
• Martin Zaefferer, 
• Olaf Mersmann

• Provides hands-on examples that illustrate how hyperparameter tuning can be applied in industry and academia
• Gives deep insights into the working mechanisms of machine learning and deep learning
• This book is open access, which means that you have free and unlimited access
• Includes code that equips readers to achieve better results with less time, costs, and effort

About this book

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. 

The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II).Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

Bibliographic Information

• Book Title Hyperparameter Tuning for Machine and Deep Learning with R
• Book Subtitle A Practical Guide
• Editors Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann
• DOI https://doi.org/10.1007/978-981-19-5170-1
• Publisher Springer Singapore
• eBook Packages Computer Science, Computer Science (R0)
• Copyright Information The Editor(s) (if applicable) and The Author(s) 2023
• Hardcover ISBN 978-981-19-5169-5Published: 02 January 2023
• Softcover ISBN 978-981-19-5172-5Published: 19 December 2022
• eBook ISBN 978-981-19-5170-1Published: 01 January 2023
• Edition Number 1
• Number of PagesXVII, 323
• Number of Illustrations 24 b/w illustrations, 60 illustrations in colour
• Topics Artificial Intelligence, Machine Learning, Statistics and Computing/Statistics Programs, Theoretical, Mathematical and Computational Physics, Computational Intelligence