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Hyperparameter tuning for machine and deep learning with Ra 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 Redited 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.
Contained By:	Springer Nature eBook
標題:	Machine learningStatistical methods.
電子資源:	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 /
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