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 01250nas 2200409- 450 001 996204080803316 005 20230425213018.0 035 $a(CKB)110978977287574 035 $a(CONSER)--2016210811 035 $a(EXLCZ)99110978977287574 100 $a20160802a20169999 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInsurance post $ethe insurance post since 1840 210 1$aLondon :$cIncisive Insurance Media,$d2016- 210 31$aLondon :$cInfoPro Digital Services 215 $a1 online resource 311 $aPrint version: Insurance post : (DLC) 2016210811 (OCoLC)957731250 1365-4284 531 $aPOST MAGAZINE AND INSURANCE WEEK 606 $aInsurance$vPeriodicals 606 $aInsurance$zGreat Britain$vPeriodicals 606 $aAssurance$vPériodiques 606 $aInsurance$2fast$3(OCoLC)fst00974532 607 $aGreat Britain$2fast 608 $aPeriodicals.$2fast 615 0$aInsurance 615 0$aInsurance 615 6$aAssurance 615 7$aInsurance. 676 $a368.941 906 $aJOURNAL 912 $a996204080803316 920 $aexl_impl conversion 996 $aInsurance post$92334467 997 $aUNISA LEADER 01440nam0 22002771i 450 001 UON00010594 005 20231205101931.828 100 $a20020107g19501954 |0itac50 ba 101 $aara 102 $aEG 105 $a|||| ||||| 200 1 $aMaq?l?t al-isl?m?y?n wa-ih?til?f al-mu?all?n$fta?l?f Ab? al-?asan ?Al? ibn Ism???l al-A??ar?$gbi-ta?q?q Mu?ammad Mu?y? al-D?n ?Abd al-?am?d 210 $aAl-Q?hira$cMaktaba al-na??a al-mi?riyya$d1369-1373 [1950-1954] 215 $a2 v.$d25 cm 606 $aISLAM$xKALAM$3UONC004503$2FI 620 $aEG$dIl Cairo$3UONL000377 686 $aARA VII AE$cPAESI ARABI - FILOSOFIA E RELIGIONE - ISLAM - DOGMATICA (Kalam)$2A 700 1$aˆal-‰A??ar?$b?Al? ibn Ism???l Ab? al-?asan$3UONV009547$01224122 702 1$a?Abd al-?am?d$bMu?ammad Mu?y? al-D?n$3UONV030274 712 $aMaktaba al-na??a al-mi?riyya$3UONV247109$4650 801 $aIT$bSOL$c20240220$gRICA 912 $aUON00010594 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI ARA VII AE 012 (1) $eSI AR 1062/1 7 012 (1) 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI ARA VII AE 012 (2) $eSI AR 1062/2 7 012 (2) 996 $aMaq?l?t al-isl?m?y?n wa-ih?til?f al-mu?all?n$92841081 997 $aUNIOR