國立高雄大學圖資館 |

Language: English

Back

Hyperparameter tuning for machine and deep learning with Ra practical guide /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hyperparameter tuning for machine and deep learning with Redited by Eva Bartz ... [et al.].
Reminder of title:	a practical guide /
other author:	Bartz, Eva.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xvii, 323 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learningStatistical methods.
Online resource:	https://doi.org/10.1007/978-981-19-5170-1
ISBN:	9789811951701$q(electronic bk.)

Hyperparameter tuning for machine and deep learning with Ra practical guide /
Hyperparameter tuning for machine and deep learning with Ra practical guide /[electronic resource] :edited by Eva Bartz ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xvii, 323 p. :ill. (some col.), digital ;24 cm.

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.

Open access.

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.

ISBN: 9789811951701$q(electronic bk.)

Standard No.: 10.1007/978-981-19-5170-1doiSubjects--Topical Terms:

305185
Machine learning
--Statistical methods.

LC Class. No.: Q325.5 / .H96 2023

Dewey Class. No.: 006.31

Hyperparameter tuning for machine and deep learning with Ra practical guide /
LDR:02844nmm a2200337 a 4500 001 644664
003 DE-He213
005 20221218135924.0
006 m d
007 cr nn 008mamaa
008 231208s2023 si s 0 eng d
020 $a 9789811951701$q(electronic bk.)
020 $a 9789811951695$q(paper)
024 7 $a 10.1007/978-981-19-5170-1 $2 doi
035 $a 978-981-19-5170-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .H96 2023
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .H998 2023
245 0 0 $a Hyperparameter tuning for machine and deep learning with R $h [electronic resource] : $b a practical guide / $c edited by Eva Bartz ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xvii, 323 p. : $b ill. (some col.), digital ; $c 24 cm.
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 $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 Machine learning $x Statistical methods. $3 305185
650 0 $a Deep learning (Machine learning) $3 913129
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Statistical Learning. $3 921460
650 2 4 $a Computational Physics and Simulations. $3 913439
650 2 4 $a Computational Intelligence. $3 338479
700 1 $a Bartz, Eva. $3 953320
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-5170-1
950 $a Computer Science (SpringerNature-11645)