LEADER 01542nam a2200361 i 4500 001 991000756779707536 005 20250414143745.0 008 940521s1994 it r |01 0 ita 020 $a8842044261 035 $ab11405429-39ule_inst 035 $aPARLA214847$9ExL 040 $aIstituto di Filosofia$bita$dSocioculturale Scs 082 04$a192$223 100 1 $aBacon, Francis$0159133 245 10$aUomo e natura :$bscritti filosofici /$cFrancesco Bacone ; a cura di Enrico De Mas, con una nota introduttiva ed una nota ai testi di Paolo Rossi 260 $aBari :$bLaterza & Figli,$c1994 300 $aXVII, 245 p. ;$c22 cm 490 1 $aBiblioteca Universale Laterza ;$v415 650 4$aFilosofia$vSaggi 650 4$aNatura$vSaggi 650 4$aUomo$vSaggi 700 1 $aRossi, Paolo 700 1 $aDe Mas, Enrico 830 0$aBiblioteca Universale Laterza ;$v415 907 $a.b11405429$b01-03-17$c01-07-02 912 $a991000756779707536 945 $aLE005 Ist.Fil. XXVI G 13$g1$i2005000245329$lle005$o-$pE0.00$q-$rl$s-$t0$u3$v0$w3$x0$y.i11593179$z01-07-02 945 $aLE005 Ist.Fil. XXVII G 17$g1$iLE004-6933$lle005$o-$pE0.00$q-$rl$s-$t0$u0$v0$w0$x0$y.i11593180$z01-07-02 945 $aLE005 Ist.Fil. XXI G 26$g1$iLE004-7148$lle005$o-$pE0.00$q-$rl$s-$t0$u0$v0$w0$x0$y.i11593192$z01-07-02 945 $aLE005 MF 24 M 25$g1$i2005000289842$lle005$o-$pE0.00$q-$rl$s-$t0$u0$v0$w0$x0$y.i11593209$z01-07-02 996 $aUomo e natura$9635659 997 $aUNISALENTO 998 $ale005$b01-01-94$cm$da$e-$fita$git$h0$i4 LEADER 03222nam 22006135 450 001 9910492147403321 005 20251204104435.0 010 $a3-030-68817-8 024 7 $a10.1007/978-3-030-68817-2 035 $a(CKB)4100000011979260 035 $a(MiAaPQ)EBC6675991 035 $a(Au-PeEL)EBL6675991 035 $a(OCoLC)1260343779 035 $a(PPN)269144420 035 $a(BIP)80869778 035 $a(BIP)78761268 035 $a(DE-He213)978-3-030-68817-2 035 $a(EXLCZ)994100000011979260 100 $a20210710d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRepresentation Learning $ePropositionalization and Embeddings /$fby Nada Lavra?, Vid Podpe?an, Marko Robnik-?ikonja 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (175 pages) 311 08$a3-030-68816-X 327 $aIntroduction to Representation Learning -- Machine Learning Background -- Text Embeddings -- Propositionalization of Relational Data -- Graph and Heterogeneous Network Transformations -- Unified Representation Learning Approaches -- Many Faces of Representation Learning. 330 $aThis monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions. 606 $aData mining 606 $aArtificial intelligence$xData processing 606 $aNumerical analysis 606 $aData Mining and Knowledge Discovery 606 $aData Science 606 $aNumerical Analysis 615 0$aData mining. 615 0$aArtificial intelligence$xData processing. 615 0$aNumerical analysis. 615 14$aData Mining and Knowledge Discovery. 615 24$aData Science. 615 24$aNumerical Analysis. 676 $a006.31 700 $aLavrac?$b Nada$0853929 702 $aPodpec?an$b Vid 702 $aRobnik-Sikonja$b Marko 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910492147403321 996 $aRepresentation Learning$92174998 997 $aUNINA