東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Hyperparameter tuning for machine an...

Bartz, Eva.

FindBook

Google Book

Amazon

博客來

Hyperparameter tuning for machine and deep learning with R = a practical guide /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hyperparameter tuning for machine and deep learning with R/ edited by Eva Bartz ... [et al.].
其他題名:	a practical guide /
其他作者:	Bartz, Eva.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xvii, 323 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Statistical Learning. -
電子資源:	https://doi.org/10.1007/978-981-19-5170-1
ISBN:	9789811951701

Hyperparameter tuning for machine and deep learning with R = a 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

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

3597795
Statistical Learning.

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

Dewey Class. No.: 006.31

Hyperparameter tuning for machine and deep learning with R = a practical guide /
LDR:02844nmm a2200337 a 4500 001 2314181
003 DE-He213
005 20221218135924.0
006 m d
007 cr nn 008mamaa
008 230902s2023 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 2 4 $a Statistical Learning. $3 3597795
650 2 4 $a Computational Physics and Simulations. $3 3538874
650 2 4 $a Computational Intelligence. $3 1001631
650 0 $a Machine learning $x Statistical methods. $3 921882
650 0 $a Deep learning (Machine learning) $3 3538509
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Bartz, Eva. $3 3625385
710 2 $a SpringerLink (Online service) $3 836513
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)