LEADER 03174nam 22005415 450 001 9910484905703321 005 20200630144823.0 010 $a3-030-36721-5 024 7 $a10.1007/978-3-030-36721-3 035 $a(CKB)4100000010480365 035 $a(DE-He213)978-3-030-36721-3 035 $a(MiAaPQ)EBC6113249 035 $a(PPN)242980716 035 $a(EXLCZ)994100000010480365 100 $a20200213d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning Architectures$b[electronic resource] $eA Mathematical Approach /$fby Ovidiu Calin 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXX, 760 p. 213 illus., 35 illus. in color.) 225 1 $aSpringer Series in the Data Sciences,$x2365-5674 311 $a3-030-36720-7 327 $aIntroductory Problems -- Activation Functions -- Cost Functions -- Finding Minima Algorithms -- Abstract Neurons -- Neural Networks -- Approximation Theorems -- Learning with One-dimensional Inputs -- Universal Approximators -- Exact Learning -- Information Representation -- Information Capacity Assessment -- Output Manifolds -- Neuromanifolds -- Pooling -- Convolutional Networks -- Recurrent Neural Networks -- Classification -- Generative Models -- Stochastic Networks -- Hints and Solutions. . 330 $aThis book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. . 410 0$aSpringer Series in the Data Sciences,$x2365-5674 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aMachine learning 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 0$aMachine learning. 615 14$aMathematical Applications in Computer Science. 615 24$aMachine Learning. 676 $a006.31 700 $aCalin$b Ovidiu$4aut$4http://id.loc.gov/vocabulary/relators/aut$0471645 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484905703321 996 $aDeep Learning Architectures$92052289 997 $aUNINA