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Each year it presents issues and trends in space policy and the space sector as a whole. Its scope is global and its perspective is European. The Yearbook also links space policy with other policy areas. It highlights specific events and issues, and provides useful insights, data and information on space activities. The first part of the Yearbook sets out a comprehensive overview of the economic, political, technological and institutional trends that have affected space activities. The second part of the Yearbook offers a more analytical perspective on the yearly ESPI theme and consists of external contributions written by professionals with diverse backgrounds and areas of expertise. The third part of the Yearbook carries forward the character of the Yearbook as an archive of space activities. The Yearbook is designed for government decision-makers and agencies, industry professionals, as well as the service sectors, researchers and scientists and the interested public. 410 0$aYearbook on Space Policy,$x1866-8305 606 $aAerospace engineering 606 $aAstronautics 606 $aPolitical science 606 $aLaw of the sea 606 $aInternational law 606 $aInternational economic relations 606 $aManagement 606 $aIndustrial management 606 $aSustainable development 606 $aAerospace Technology and Astronautics$3https://scigraph.springernature.com/ontologies/product-market-codes/T17050 606 $aPolitical Science$3https://scigraph.springernature.com/ontologies/product-market-codes/911000 606 $aLaw of the Sea, Air and Outer Space$3https://scigraph.springernature.com/ontologies/product-market-codes/R19060 606 $aInternational Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W33000 606 $aInnovation/Technology Management$3https://scigraph.springernature.com/ontologies/product-market-codes/518000 606 $aSustainable Development$3https://scigraph.springernature.com/ontologies/product-market-codes/U34000 615 0$aAerospace engineering. 615 0$aAstronautics. 615 0$aPolitical science. 615 0$aLaw of the sea. 615 0$aInternational law. 615 0$aInternational economic relations. 615 0$aManagement. 615 0$aIndustrial management. 615 0$aSustainable development. 615 14$aAerospace Technology and Astronautics. 615 24$aPolitical Science. 615 24$aLaw of the Sea, Air and Outer Space. 615 24$aInternational Economics. 615 24$aInnovation/Technology Management. 615 24$aSustainable Development. 676 $a500.5 702 $aAl-Ekabi$b Cenan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aFerretti$b Stefano$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299949803321 996 $aYearbook on Space Policy 2016$92497223 997 $aUNINA LEADER 02945nam 22005295 450 001 9910484905703321 005 20251113184845.0 010 $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 $eA Mathematical Approach /$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 $aSpringer Series in the Data Sciences,$x2365-5682 311 08$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. 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