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    Hyperparameter Tuning for Machine and Deep Learning with R

    A Practical Guide

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    Contributor(s)
    Bartz, Eva (editor)
    Bartz-Beielstein, Thomas (editor)
    Zaefferer, Martin (editor)
    Mersmann, Olaf (editor)
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/60840
    Keywords
    Hyperparameter Tuning; Hyperparameters; Tuning; Deep Neural Networks; Reinforcement Learning; Machine Learning; Textbook
    DOI
    10.1007/978-981-19-5170-1
    ISBN
    9789811951701, 9789811951701
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Singapore, 2023
    Imprint
    Springer Nature Singapore
    Classification
    Artificial intelligence
    Machine learning
    Mathematical and statistical software
    Mathematical physics
    Pages
    323
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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