Hyperparameter Tuning for Machine and Deep Learning with R : : A Practical Guide.
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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Place / Publishing House: | Singapore : : Springer,, 2023. ©2023. |
Year of Publication: | 2023 |
Edition: | 1st ed. |
Language: | English |
Physical Description: | 1 electronic resource (323 p.) |
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(CKB)5840000000221153 (oapen)https://directory.doabooks.org/handle/20.500.12854/96206 (MiAaPQ)EBC7165982 (Au-PeEL)EBL7165982 (OCoLC)1361718967 (OCoLC)1372397469 (OCoLC)1375294844 (OCoLC)1378936185 (PPN)267816472 (EXLCZ)995840000000221153 |
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Bartz, Eva. Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. 1st ed. Singapore : Springer, 2023. ©2023. 1 electronic resource (323 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English Description based on publisher supplied metadata and other sources. Artificial intelligence bicssc Machine learning bicssc Mathematical & statistical software bicssc Mathematical physics bicssc Hyperparameter Tuning Hyperparameters Tuning Deep Neural Networks Reinforcement Learning Machine Learning 981-19-5169-1 Bartz-Beielstein, Thomas. Zaefferer, Martin. Mersmann, Olaf. |
language |
English |
format |
eBook |
author |
Bartz, Eva. |
spellingShingle |
Bartz, Eva. Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
author_facet |
Bartz, Eva. Bartz-Beielstein, Thomas. Zaefferer, Martin. Mersmann, Olaf. |
author_variant |
e b eb |
author2 |
Bartz-Beielstein, Thomas. Zaefferer, Martin. Mersmann, Olaf. |
author2_variant |
t b b tbb m z mz o m om |
author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
author_sort |
Bartz, Eva. |
title |
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
title_sub |
A Practical Guide. |
title_full |
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
title_fullStr |
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
title_full_unstemmed |
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
title_auth |
Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
title_new |
Hyperparameter Tuning for Machine and Deep Learning with R : |
title_sort |
hyperparameter tuning for machine and deep learning with r : a practical guide. |
publisher |
Springer, |
publishDate |
2023 |
physical |
1 electronic resource (323 p.) |
edition |
1st ed. |
isbn |
981-19-5170-5 981-19-5169-1 |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q334-342 |
callnumber-sort |
Q 3334 3342 |
illustrated |
Not Illustrated |
oclc_num |
1361718967 1372397469 1375294844 1378936185 |
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(CKB)5840000000221153 (oapen)https://directory.doabooks.org/handle/20.500.12854/96206 (MiAaPQ)EBC7165982 (Au-PeEL)EBL7165982 (OCoLC)1361718967 (OCoLC)1372397469 (OCoLC)1375294844 (OCoLC)1378936185 (PPN)267816472 (EXLCZ)995840000000221153 |
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Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. |
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