03480nam 22006373 450 99654682930331620230317084551.0981-19-5170-5(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(EXLCZ)99584000000022115320230317d2023 uy 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierHyperparameter Tuning for Machine and Deep Learning with R A Practical Guide1st ed.Singapore :Springer,2023.©2023.1 electronic resource (323 p.)981-19-5169-1 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 case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. 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 intelligencebicsscMachine learningbicsscMathematical & statistical softwarebicsscMathematical physicsbicsscHyperparameter TuningHyperparametersTuningDeep Neural NetworksReinforcement LearningMachine LearningArtificial intelligenceMachine learningMathematical & statistical softwareMathematical physicsBartz Eva1423767Bartz-Beielstein Thomas1337543Zaefferer Martin1337544Mersmann Olaf1337545MiAaPQMiAaPQMiAaPQBOOK996546829303316Hyperparameter Tuning for Machine and Deep Learning with R3552201UNISA