LEADER 01242nam0-22003971i-450- 001 990005511400203316 005 20040610120000.0 035 $a000551140 035 $aUSA01000551140 035 $a(ALEPH)000551140USA01 035 $a000551140 100 $a20030508d2001-------|0enac50------ba 101 $aeng 102 $aGB 105 $a|||| ||||| 200 1 $aSustainable fischery systems$fAntony T. Charles 210 $aOxford$cBlackwell Science Ltd$d2001 215 $aXIV, 370 p.$cill.$d25 cm 225 2$aFish and aquatic resources series. - Oxford$v5 410 1$12001$aFish and aquatic resources series. - Oxford$v5 606 $aPesca$xSviluppo sostenibile$2FI 620 $dOxford 676 $a333.95616$cRisorse marine. Pesci$v21 700 1$aCHARLES,$bAntony Trevor$0614161 712 $aBlackwell Publishers 801 $aIT$bSOL$c20120104 912 $a990005511400203316 950 $aDIP.TO SCIENZE ECONOMICHE - (SA)$dDS 300 333.95616 CHA$e11694 DISES 951 $a300 333.95616 CHA$b11694 DISES 959 $aBK 969 $aDISES 979 $c20121027$lUSA01$h1532 979 $c20121027$lUSA01$h1613 996 $aSustainable fischery systems$91130089 997 $aUNISA NUM $aUSA12112 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