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020 _a9789811951701
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024 7 _a10.1007/978-981-19-5170-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aHyperparameter Tuning for Machine and Deep Learning with R
_h[electronic resource] :
_bA Practical Guide /
_cedited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXVII, 323 p. 84 illus., 60 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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.
506 0 _aOpen Access
520 _aThis 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.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 0 _aMathematical physics.
650 0 _aComputer simulation.
650 0 _aComputational intelligence.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aStatistical Learning.
650 2 4 _aComputational Physics and Simulations.
650 2 4 _aComputational Intelligence.
700 1 _aBartz, Eva.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBartz-Beielstein, Thomas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aZaefferer, Martin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMersmann, Olaf.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811951695
776 0 8 _iPrinted edition:
_z9789811951718
776 0 8 _iPrinted edition:
_z9789811951725
856 4 0 _uhttps://doi.org/10.1007/978-981-19-5170-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
942 _cSPRINGER
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