LEADER 00879nam0-22003011i-450- 001 990004486790403321 005 20120619105319.0 035 $a000448679 035 $aFED01000448679 035 $a(Aleph)000448679FED01 035 $a000448679 100 $a19990604d1913----km-y0itay50------ba 101 0 $afre 105 $ay-------001yy 200 1 $a<>unité morale des religions$fpar Gaston Bonet-Maury 210 $aParis$cLibrairie Felix Alcan$d1913 215 $a214 p.$d19 cm 225 1 $aBibliothèque de philosophie contemporaine 610 0 $aMorale e religione$aSistemi etici 676 $a205 700 1$aBonet-Maury,$bGaston$0179329 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004486790403321 952 $a205 BON 1$bBibl. 822/4700$fFLFBC 959 $aFLFBC 996 $aUnité morale des religions$9546710 997 $aUNINA LEADER 03408nam 22007215 450 001 9910427707703321 005 20251113174314.0 010 $a3-030-60457-8 024 7 $a10.1007/978-3-030-60457-8 035 $a(CKB)4100000011493455 035 $a(DE-He213)978-3-030-60457-8 035 $a(MiAaPQ)EBC6369422 035 $a(PPN)255063903 035 $a(EXLCZ)994100000011493455 100 $a20201006d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNatural Language Processing and Chinese Computing $e9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14?18, 2020, Proceedings, Part II /$fedited by Xiaodan Zhu, Min Zhang, Yu Hong, Ruifang He 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXX, 591 p. 317 illus., 146 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12431 311 08$a3-030-60456-X 320 $aIncludes bibliographical references and index. 327 $aTrending Topics (Explainability, Ethics, Privacy, Multimodal NLP) -- Poster -- Explainable AI Workshop -- Student Workshop -- Evaluation Workshop. 330 $aThis two-volume set of LNAI 12340 and LNAI 12341 constitutes the refereed proceedings of the 9th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2020, held in Zhengzhou, China, in October 2020. The 70 full papers, 30 poster papers and 14 workshop papers presented were carefully reviewed and selected from 320 submissions. They are organized in the following areas: Conversational Bot/QA; Fundamentals of NLP; Knowledge Base, Graphs and Semantic Web; Machine Learning for NLP; Machine Translation and Multilinguality; NLP Applications; Social Media and Network; Text Mining; and Trending Topics. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12431 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aComputer science 606 $aSocial sciences$xData processing 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aTheory of Computation 606 $aComputer Application in Social and Behavioral Sciences 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aComputer science. 615 0$aSocial sciences$xData processing. 615 14$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aTheory of Computation. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a006.35 702 $aZhu$b Xiaodan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427707703321 996 $aNatural Language Processing and Chinese Computing$91912515 997 $aUNINA LEADER 04164nam 22006015 450 001 9910919815903321 005 20251113191236.0 010 $a9798868810350$b(electronic bk.) 010 $z9798868810343 024 7 $a10.1007/979-8-8688-1035-0 035 $a(MiAaPQ)EBC31862043 035 $a(Au-PeEL)EBL31862043 035 $a(CKB)37083962500041 035 $a(DE-He213)979-8-8688-1035-0 035 $a(CaSebORM)9798868810350 035 $a(OCoLC)1482310053 035 $a(OCoLC-P)1482310053 035 $a(OCoLC)1482817132 035 $a(EXLCZ)9937083962500041 100 $a20241227d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHands-on Deep Learning $eA Guide to Deep Learning with Projects and Applications /$fby Harsh Bhasin 205 $a1st ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (373 pages) 225 0 $aProfessional and Applied Computing Series 311 08$aPrint version: Bhasin, Harsh Hands-On Deep Learning Berkeley, CA : Apress L. P.,c2025 9798868810343 327 $aChapter 1: Revisiting Machine Learning -- Chapter 2: Introduction to Deep Learning -- Chapter 3: Neural Networks -- Chapter 4: Training Deep Networks -- Chapter 5: Hyperparameter Tuning -- Chapter 6: Convolutional Neural Networks: Part 1 -- Chapter 7: Convolutional Neural Networks : Part 2 -- Chapter 8: Transfer Learning -- Chapter 9: Recurrent Neural Networks -- Chapter 10: LSTM and GRU -- Chapter 11: Autoencoders -- Chapter 12: Introduction to Generative Models -- Appendices A-G. 330 $aThis book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will Learn What are deep neural networks? What is transfer learning, multi-task learning, and end-to-end learning? What are hyperparameters, bias, variance, and data division? What are CNN and RNN? . 606 $aArtificial intelligence 606 $aMachine learning 606 $aPython (Computer program language) 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aPython 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aPython. 676 $a006.3 700 $aBhasin$b Harsh$01778243 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910919815903321 996 $aHands-on Deep Learning$94306451 997 $aUNINA