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008 180911s2016    maua     b    001 0 eng d
020   $a9780262035613
035   $ab14354706-39ule_inst
040   $aBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. Ingegneria Innovazione$beng
082 00$a006.31$223
100 1 $aGoodfellow, Ian$0752902
245 10$aDeep learning /$cIan Goodfellow, Yoshua Bengio, and Aaron Courville
264  1$aCambridge, Massachusetts :$bThe MIT Press,$cc2016
300   $axxii, 775 p. :$bill. (some color) ;$c24 cm
490 0 $aAdaptive computation and machine learning
504   $aIncludes bibliographical references and index
505 0 $aApplied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
650  0$aMachine learning
700 1 $aBengio, Yoshua$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0752903
700 1 $aCourville, Aaron
907   $a.b14354706$b04-12-18$c29-11-18
912   $a991003577709707536
945   $aLE026 006.31 GOO 01.01 2016$g1$i2026000073033$lle026$nProf. Ricci / Biblioteca$op$pE82.50$q-$rl$s-  $t4$u5$v0$w5$x0$y.i15870753$z04-12-18
996   $aDeep learning$91749342
997   $aUNISALENTO
998   $ale026$b11-09-18$cm$da  $e $feng$gmau$h0$i0