LEADER 01463nam a2200289 i 4500 001 991003853299707536 008 080904s2008 it 000 0 ita d 020 $a8814127700 035 $ab13761134-39ule_inst 040 $aDip.to Studi Giuridici$bita 082 0 $a341.2422026 245 00$aCodice dell'Unione europea :$bil Trattato sull'Unione europea e il trattato istitutivo della Comunità europea modificati dai Trattati di Maastricht, di Amsterdam e di Nizza e dai trattati di adesione, con annotazioni di giurisprudenza della Corte di giustizia e del Tribunale di primo grado : i documenti rilevanti /$cLuigi Ferrari Bravo, Alfredo Rizzo 250 $a3. ed. / curata da Alfredo Rizzo e Francesco M. Di Majo 260 $aMilano :$bA. Giuffrè,$c2008 300 $axlvi, 1457 p. ;$c24 cm 440 0$aCodici Giuffre annotati con la giurisprudenza 610 04$aCorte di giustizia delle Comunità europee$xGiurisprudenza 651 4$aUnione Europea$vTrattati 700 1 $aFerrari Bravo, Luigi $eauthor$4http://id.loc.gov/vocabulary/relators/aut$0135051 700 1 $aDi Maio, Francesco M. 700 1 $aRizzo, Vincenzo 907 $a.b13761134$b02-04-14$c04-09-08 912 $a991003853299707536 945 $aLE027 341.24 CUE01.01$g1$i2027000187522$lle027$o-$pE110.00$q-$rn$so $t0$u0$v0$w0$x0$y.i14843055$z29-09-08 996 $aCodice dell'Unione europea$91464652 997 $aUNISALENTO 998 $ale027$b04-09-08$cm$da $e-$fita$git $h0$i0 LEADER 02805nam0-2200469---450 001 9910291458003321 005 20240116103445.0 012 $aD.ES e-de 6.ie t.ur (3) 1806 (R)$2fei$5IT-NA0338: A II 58 (1 012 $aD.ES e-de 6.ie t.ur (3) 1806 (R)$2fei$5IT-NA0338: H VIII 23 (1 012 $aDID. a,u- 7.s. casu (3) 1806 (Q)$2fei$5IT-NA0338: A II 58 (2 012 $aDID. a,u- 7.s. casu (3) 1806 (Q)$2fei$5IT-NA0338: H VIII 23 (2 100 $a20181115h18061807km-y0itay50------ba 101 0 $alat 102 $aIT 140 $aa-----------------bb0------- 200 1 $aSicularum plantarum centuria prima [-secunda] Antonini Bivona Bernardi Accademiae economicae Florentinae socii corresp. 210 $aPanormi$capud Philippum Barravecchia$d1806$eDabam Panormi$hdie 2. decem. 1807 215 $a2 v.$cill. calcogr.$d4° 300 $aV. 2 introdotto da occhietto 306 $aColophon dal v. 2 307 $aSegn.: [a]? b-k? l² (v. 1) 307 $aSegn.: [a]? b-k? 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Orto Botanico di Napoli Biblioteca Tenoreana" sul front.$5IT-NA0338: A II 58 (1-2 321 1 $aPritzel$b1872$c805$uhttps://books.google.it/books?id=_FnAgYaaY0AC&printsec=frontcover&hl=it&source=gbs_ge_summary_r&cad=0#v=onepage&q=bivona&f=false 620 $aItalia.$dPalermo 700 1$aBivona Bernardi,$bAntonino$0760137 719 00$aBarravecchia,$gFilippo$4650 801 0$aIT$bUNINA$gREICAT$2UNIMARC 856 4 $zVisualizza la versione elettronica in Suchmaschine des Österreichischen Bibliothekenverbundes$uhttp://bibdigital.rjb.csic.es/ing/Libro.php?Libro=4286&Pagina=1$e20181115 856 4 $zVisualizza la versione elettronica in Suchmaschine des Österreichischen Bibliothekenverbundes$uhttp://bibdigital.rjb.csic.es/ing/Libro.php?Libro=4288&Pagina=1$e20181115 901 $aAQ 912 $a9910291458003321 952 $aA II 58 (1$b36$fDBV 952 $aA II 58 (2$fDBV 952 $aH VIII 23 (1$b36$fDBV 952 $aH VIII 23 (2$fDBV 959 $aDBV 996 $aSicularum plantarum centuria prima Antonini Bivona Bernardi Accademiae economicae Florentinae socii corresp$91537533 997 $aUNINA LEADER 04352nam 22007935 450 001 9910637747703321 005 20251113185255.0 010 $a9789811951701 010 $a9811951705 024 7 $a10.1007/978-981-19-5170-1 035 $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(ODN)ODN0010070573 035 $a(DE-He213)978-981-19-5170-1 035 $a(EXLCZ)995840000000221153 100 $a20221218d2023 u| 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 $eA Practical Guide /$fedited by Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 electronic resource (323 p.) 311 08$a9789811951695 311 08$a9811951691 327 $aChapter 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. 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 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 606 $aMachine learning 606 $aMathematical physics 606 $aComputer simulation 606 $aComputational intelligence 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aStatistical Learning 606 $aComputational Physics and Simulations 606 $aComputational Intelligence 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aMathematical physics. 615 0$aComputer simulation. 615 0$aComputational intelligence. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aStatistical Learning. 615 24$aComputational Physics and Simulations. 615 24$aComputational Intelligence. 676 $a006.3 686 $aCOM004000$aCOM077000$aSCI040000$aTEC009000$2bisacsh 700 $aBartz$b Eva 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 $a9910637747703321 996 $aHyperparameter Tuning for Machine and Deep Learning with R$93057013 997 $aUNINA