LEADER 03605nam 22006255 450 001 9910350219603321 005 20220627194635.0 010 $a981-13-5850-8 024 7 $a10.1007/978-981-13-5850-0 035 $a(CKB)5340000000061437 035 $a(MiAaPQ)EBC5925561 035 $a(DE-He213)978-981-13-5850-0 035 $a(PPN)235666971 035 $a(EXLCZ)995340000000061437 100 $a20190413d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning with R /$fby Abhijit Ghatak 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (259 pages) 311 $a981-13-5849-4 320 $aIncludes bibliographical references. 327 $aIntroduction to Machine Learning -- Introduction to Neural Networks -- Deep Neural Networks ? I -- Initialization of Network Parameters -- Optimization -- Deep Neural Networks - II -- Convolutional Neural Networks (ConvNets) -- Recurrent Neural Networks (RNN) or Sequence Models -- Epilogue. 330 $aDeep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks. . 606 $aArtificial intelligence 606 $aComputer science?Mathematics 606 $aComputer programming 606 $aStatistics  606 $aR (Computer program language) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematics of Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I17001 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 615 0$aArtificial intelligence. 615 0$aComputer science?Mathematics. 615 0$aComputer programming. 615 0$aStatistics . 615 0$aR (Computer program language). 615 14$aArtificial Intelligence. 615 24$aMathematics of Computing. 615 24$aProgramming Techniques. 615 24$aStatistics and Computing/Statistics Programs. 676 $a006.31 700 $aGhatak$b Abhijit$4aut$4http://id.loc.gov/vocabulary/relators/aut$01057986 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910350219603321 996 $aDeep Learning with R$92496353 997 $aUNINA