LEADER 00870nam0 22002653i 450 001 TO00042960 005 20231121125833.0 010 $a2877420469 100 $a20130205d1990 ||||0itac50 ba 101 | $afre 102 $abe 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $a˜La œlegende des petits matins$fJean-Claude Pirotte 210 $aLevallois-Perret$cManya$dc1990 215 $a139 p.$d21 cm 700 1$aPirotte$b, Jean-Claude$3BVEV004039$4070$0440157 801 3$aIT$bIT-01$c20130205 850 $aIT-FR0017 899 $aBiblioteca umanistica Giorgio Aprea$bFR0017 912 $aTO00042960 950 0$aBiblioteca umanistica Giorgio Aprea$d 52MAG 9/1637$e 52FLS0000010055 VMN RS $fA $h20130205$i20130205 977 $a 52 996 $aLégende des petits matins$91255642 997 $aUNICAS LEADER 03871nam 2200913z- 450 001 9910557610303321 005 20220321 035 $a(CKB)5400000000045299 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/79633 035 $a(oapen)doab79633 035 $a(EXLCZ)995400000000045299 100 $a20202203d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aComputational Optimizations for Machine Learning 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (276 p.) 311 08$a3-0365-3186-6 311 08$a3-0365-3187-4 330 $aThe present book contains the 10 articles finally accepted for publication in the Special Issue "Computational Optimizations for Machine Learning" of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity. 606 $aMathematics & science$2bicssc 606 $aResearch & information: general$2bicssc 610 $aARIMA model 610 $aartificial intelligence 610 $aautoencoders 610 $abed roughness 610 $abio-inspired algorithms 610 $aCNN architecture 610 $acomputational intelligence 610 $aconvolutional neural network 610 $adeep compression 610 $adeep learning 610 $adeep neural networks 610 $aDNN 610 $aenergy dissipation 610 $aevolution of weights 610 $aevolutionary algorithms 610 $aevolutionary computation 610 $afeature selection 610 $afloating-point numbers 610 $aFLOW-3D 610 $ageneralization error 610 $agenetic algorithms 610 $ahardware acceleration 610 $aHeating, Ventilation and Air Conditioning (HVAC) 610 $ahydraulic jumps 610 $alow power 610 $amachine learning 610 $ameta-heuristic optimization 610 $ametaheuristics search 610 $amodel predictive control 610 $amulti-objective optimization 610 $anature inspired algorithms 610 $aneural networks 610 $anonlinear systems 610 $aonline model selection 610 $aonline optimization 610 $aprecipitation nowcasting 610 $aquantization 610 $aradar data 610 $arecurrent neural networks 610 $aReLU 610 $asensitivity analysis 610 $asmart building 610 $asoft computing 610 $aswarm intelligence 610 $atime series analysis 610 $atraining 615 7$aMathematics & science 615 7$aResearch & information: general 700 $aGabbay$b Freddy$4edt$01304465 702 $aGabbay$b Freddy$4oth 906 $aBOOK 912 $a9910557610303321 996 $aComputational Optimizations for Machine Learning$93027447 997 $aUNINA