04352nam 22007935 450 991063774770332120251113185255.09789811951701981195170510.1007/978-981-19-5170-1(CKB)5840000000221153(oapen)https://directory.doabooks.org/handle/20.500.12854/96206(MiAaPQ)EBC7165982(Au-PeEL)EBL7165982(OCoLC)1361718967(OCoLC)1372397469(OCoLC)1375294844(OCoLC)1378936185(PPN)267816472(ODN)ODN0010070573(DE-He213)978-981-19-5170-1(EXLCZ)99584000000022115320221218d2023 u| 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierHyperparameter Tuning for Machine and Deep Learning with R A Practical Guide /edited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 electronic resource (323 p.)9789811951695 9811951691 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.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.Artificial intelligenceMachine learningMathematical physicsComputer simulationComputational intelligenceArtificial IntelligenceMachine LearningStatistical LearningComputational Physics and SimulationsComputational IntelligenceArtificial intelligence.Machine learning.Mathematical physics.Computer simulation.Computational intelligence.Artificial Intelligence.Machine Learning.Statistical Learning.Computational Physics and Simulations.Computational Intelligence.006.3COM004000COM077000SCI040000TEC009000bisacshBartz EvaBartz-Beielstein Thomas1337543Zaefferer Martin1337544Mersmann Olaf1337545MiAaPQMiAaPQMiAaPQBOOK9910637747703321Hyperparameter Tuning for Machine and Deep Learning with R3057013UNINA