LEADER 03480nam 22006373 450 001 996546829303316 005 20230317084551.0 010 $a981-19-5170-5 035 $a(CKB)5840000000221153 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/96206 035 $a(MiAaPQ)EBC7165982 035 $a(Au-PeEL)EBL7165982 035 $a(OCoLC)1361718967 035 $a(OCoLC)1372397469 035 $a(OCoLC)1375294844 035 $a(OCoLC)1378936185 035 $a(PPN)267816472 035 $a(EXLCZ)995840000000221153 100 $a20230317d2023 uy 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHyperparameter Tuning for Machine and Deep Learning with R $eA Practical Guide 205 $a1st ed. 210 1$aSingapore :$cSpringer,$d2023. 210 4$d©2023. 215 $a1 electronic resource (323 p.) 311 $a981-19-5169-1 330 $aThis 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. 606 $aArtificial intelligence$2bicssc 606 $aMachine learning$2bicssc 606 $aMathematical & statistical software$2bicssc 606 $aMathematical physics$2bicssc 610 $aHyperparameter Tuning 610 $aHyperparameters 610 $aTuning 610 $aDeep Neural Networks 610 $aReinforcement Learning 610 $aMachine Learning 615 7$aArtificial intelligence 615 7$aMachine learning 615 7$aMathematical & statistical software 615 7$aMathematical physics 700 $aBartz$b Eva$01423767 701 $aBartz-Beielstein$b Thomas$01337543 701 $aZaefferer$b Martin$01337544 701 $aMersmann$b Olaf$01337545 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546829303316 996 $aHyperparameter Tuning for Machine and Deep Learning with R$93552201 997 $aUNISA LEADER 00901nam0-2200289 --450 001 9910640689403321 005 20241016131158.0 100 $a20230203d1959----kmuy0itay5050 ba 101 0 $afre 102 $aFR 105 $a 001yy 200 1 $a<>pouvoir discretionnaire$fpar Jean-Claude Venezia$gpréface de Jean Rivero 210 $aParis$cLibrairie Générale de droit et de jurisprudence Pichon et Durand-Auzias$d1959 215 $aIV, 175 p.$d25 cm 225 1 $aBibliothèque de droit public$v17 676 $a342.44066$v22 700 1$aVenezia,$bJean-Claude$0235938 702 1$aRivero,$bJean$f<1910-2001> 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910640689403321 952 $aCOLLEZ. 77 (17)$b55861$fFGBC 952 $aF CN 4(5)$b130$fDDCIC 959 $aDDCIC 959 $aFGBC 996 $aPouvoir discretionnaire$94239491 997 $aUNINA