• Title: Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
• Author(s) Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann
• Publisher: Springer; 1st ed. 2023 edition (December 19, 2022); eBook (Creative Commons Licensed)
• License(s): CC BY 4.0
• Hardcover: 340 pages
• eBook: PDF and ePub
• Language: English
• ISBN-10: 9811951721
• ISBN-13: 978-9811951725
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Book Description

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.

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• Machine Learning
• Deep Learning and Neural Networks
• R Programming
• Artificial Intelligence

• Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
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