LEADER 02535nam 2200589 a 450 001 9910778646103321 005 20230422031350.0 010 $a0-309-17327-2 010 $a1-282-08371-6 010 $a9786612083716 010 $a0-309-51798-2 010 $a0-585-05854-7 035 $a(CKB)110986584752636 035 $a(EBL)3375644 035 $a(SSID)ssj0000150731 035 $a(PQKBManifestationID)11910587 035 $a(PQKBTitleCode)TC0000150731 035 $a(PQKBWorkID)10281257 035 $a(PQKB)11198738 035 $a(MiAaPQ)EBC3375644 035 $a(Au-PeEL)EBL3375644 035 $a(CaPaEBR)ebr10041064 035 $a(OCoLC)885455376 035 $a(EXLCZ)99110986584752636 100 $a19990325d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aEvaluating federal research programs$b[electronic resource] $eresearch and the Government Performance and Results Act /$fCommittee on Science, Engineering, and Public Policy, National Academy of Sciences, National Academy of Engineering, Institute of Medicine 210 $aWashington, D.C. $cNational Academy Press$d1999 215 $a1 online resource (94 p.) 300 $aDescription based upon print version of record. 311 $a0-309-06430-9 320 $aIncludes bibliographical references (p. 79-80). 327 $aFront Matter; Preface; ACKNOWLEDGMENTS; Contents; Executive Summary; CHAPTER 1 Statement of the Problem; CHAPTER 2 Research and the Federal Government; CHAPTER 3 Measuring AND EVALUATING Federally Funded Research; CHAPTER 4 Recommendations; APPENDIX A COMMITTEE ON SCIENCE, ENGINEERING, AND PUBLIC POLICY MEMBERS' BIOGRAPHICAL INFORMATION; APPENDIX B HOUSE SCIENCE COMMITTEE LETTER; APPENDIX C PROJECT SUMMARY; APPENDIX D government performance and results act; APPENDIX E REFERENCES 606 $aResearch$zUnited States$xEvaluation 606 $aEngineering$xResearch$zUnited States$xEvaluation 606 $aAdministrative agencies$zUnited States$xManagement$xEvaluation 615 0$aResearch$xEvaluation. 615 0$aEngineering$xResearch$xEvaluation. 615 0$aAdministrative agencies$xManagement$xEvaluation. 676 $a507/.2073 712 02$aCommittee on Science, Engineering, and Public Policy (U.S.) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778646103321 996 $aEvaluating federal research programs$93753070 997 $aUNINA LEADER 03824nam 2200433 450 001 9910793816103321 005 20200302093010.0 010 $a1-78953-498-4 035 $a(CKB)4100000010011013 035 $a(MiAaPQ)EBC6005546 035 $a(CaSebORM)9781789538779 035 $a(PPN)242831737 035 $a(EXLCZ)994100000010011013 100 $a20200302d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced deep learning with R $ebecome an expert at designing, building, and improving advanced neural network models using R /$fBharatendra Rai 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai :$cPackt,$d[2019] 210 4$dİ2019 215 $a1 online resource (vii, 337 pages) $cillustrations 300 $aIncludes index. 311 $a1-78953-877-7 320 $aIncludes bibliographical references. 330 $aDiscover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the... 606 $aR (Computer program language) 615 0$aR (Computer program language) 676 $a519.502855133 700 $aRai$b Bharatendra$01524690 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910793816103321 996 $aAdvanced deep learning with R$93765703 997 $aUNINA