Hyperparameter Tuning for Machine and Deep Learning with R [electronic resource] : A Practical Guide / edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann.

Unlimited

Open access

Open access

Hyperparameter Tuning for Machine and Deep Learning with R [electronic resource] : A Practical Guide / edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann.

First edition 2023.

9789811951701
9811951705
9789811951695 (print)

Singapore : Springer Nature Singapore : Imprint: Springer, 2023.

English

English.

1 online resource (XVII, 323 pages 84 illustrations, 60 illustrations in color. :) : online resource.

10.1007/978-981-19-5170-1 doi

Q334-342

006.3/1

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 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.

Includes bibliographical references and index.

Open access.

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Printed edition 9789811951695
Printed edition 9789811951718
Printed edition 9789811951725

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