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...

Full description

Saved in:
Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Bartz, Eva (Editor), Bartz-Beielstein, Thomas (Editor), Zaefferer, Martin (Editor), Mersmann, Olaf (Editor)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edition:1st ed. 2023.
Subjects:
Online Access:Link to Metadata
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000nam a22000005i 4500
001 978-981-19-5170-1
003 DE-He213
005 20221218135924.0
007 cr nn 008mamaa
008 221218s2023 si | s |||| 0|eng d
020 |a 9789811951701  |9 978-981-19-5170-1 
024 7 |a 10.1007/978-981-19-5170-1  |2 doi 
050 4 |a Q334-342 
050 4 |a TA347.A78 
072 7 |a UYQ  |2 bicssc 
072 7 |a COM004000  |2 bisacsh 
072 7 |a UYQ  |2 thema 
082 0 4 |a 006.3  |2 23 
245 1 0 |a Hyperparameter Tuning for Machine and Deep Learning with R  |h [electronic resource] :  |b A Practical Guide /  |c edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann. 
250 |a 1st ed. 2023. 
264 1 |a Singapore :  |b Springer Nature Singapore :  |b Imprint: Springer,  |c 2023. 
300 |a XVII, 323 p. 84 illus., 60 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a 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. 
506 0 |a Open Access 
520 |a 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. 
650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
650 0 |a Mathematical physics. 
650 0 |a Computer simulation. 
650 0 |a Computational intelligence. 
650 1 4 |a Artificial Intelligence. 
650 2 4 |a Machine Learning. 
650 2 4 |a Statistical Learning. 
650 2 4 |a Computational Physics and Simulations. 
650 2 4 |a Computational Intelligence. 
700 1 |a Bartz, Eva.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Bartz-Beielstein, Thomas.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Zaefferer, Martin.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Mersmann, Olaf.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9789811951695 
776 0 8 |i Printed edition:  |z 9789811951718 
776 0 8 |i Printed edition:  |z 9789811951725 
856 4 0 |u https://doi.org/10.1007/978-981-19-5170-1  |z Link to Metadata 
912 |a ZDB-2-SCS 
912 |a ZDB-2-SXCS 
912 |a ZDB-2-SOB 
950 |a Computer Science (SpringerNature-11645) 
950 |a Computer Science (R0) (SpringerNature-43710)