TY  - BOOK
AU  - Bartz,Eva
AU  - Bartz-Beielstein,Thomas
AU  - Zaefferer,Martin
AU  - Mersmann,Olaf
ED  - SpringerLink (Online service)
TI  - Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide 
SN  - 9789811951701
AV  - Q334-342 
U1  - 006.3 23
PY  - 2023///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Artificial intelligence
KW  - Machine learning
KW  - Mathematical physics
KW  - Computer simulation
KW  - Computational intelligence
KW  - Artificial Intelligence
KW  - Machine Learning
KW  - Statistical Learning
KW  - Computational Physics and Simulations
KW  - Computational Intelligence
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-981-19-5170-1
ER  -