Hyperparameter Tuning for Machine and Deep Learning with R [E-Book] : A Practical Guide / edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann.
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 a...
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Full text |
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| Personal Name(s): | Bartz, Eva, editor |
| Bartz-Beielstein, Thomas, editor / Mersmann, Olaf, editor / Zaefferer, Martin, editor | |
| Edition: |
1st edition 2023. |
| Imprint: |
Singapore :
Springer,
2023
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| Physical Description: |
XVII, 323 pages 84 illustrations, 60 illustrations in color (online resource) |
| Note: |
englisch |
| ISBN: |
9789811951701 |
| DOI: |
10.1007/978-981-19-5170-1 |
| Subject (LOC): |
- Chapter 1: Introduction
- Chapter 2: Tuning
- Chapter 3: Models
- Hyperparameter Tuning Approaches
- Chapter 5: Result Aggregation
- Chapter 6: Relevance of Tuning in Industrial Applications
- Chapter 7: Hyperparameter Tuning in German Official Statistics
- Chapter 8: Case Study I
- Chapter 9: Case Study II
- Chapter 10: Case Study III
- Chapter IV: Case Study IV
- Chapter 12: Global Study.