Computational methods for deep learning: theoretic, practice and applications
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cham, Switzerland
Springer
[2021]
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| Series: | Texts in computer science
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xvii, 134 Seiten Illustrationen, Diagramme, 1 Karte (teilweise farbig) |
| ISBN: | 9783030610807 |
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Record in the Search Index
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| adam_text | Contents 1 Introduction................................................................................................. 1.1 Introduction ................................................................................. 1.2 Deep Learning....................................................................................... 1.3 The Chronicle of Deep Learning........................................................ 1.4 Our Deep Learning Projects................................................................ 1.5 Awarded Work in Deep Learning...................................................... 1.6 Questions.......................... References...................................................................................................... 1 1 3 6 12 14 15 15 2 Deep Learning Platforms.......................................................................... 2.1 Introduction.......................................................................... 2.2 MATLAB for Deep Learning............................................................. 2.3 TensorFlow for Deep Learning........................................................... 2.4 Data Augmentation................................................................................ 2.5 Fundamental Mathematics.................................................................... 2.6 Questions................................................................................................ References............................................................................................ 21 21 22 26 31 32 36 37 3 CNN and
RNN............................................................................................ 3.1 CNN and YOLO.................................................................................. 3.1.1 R-CNN...................................................................................... 3.1.2 Mask R-CNN........................................................................... 3.1.3 YOLO ...................................................................................... 3.1.4 SSD........................................................................................... 3.1.5 DenseNets and ResNets........................................................... 3.2 RNN and Time Series Analysis........................................................... 3.3 HMM..................................................................................................... 3.3.1 RNN: Recurrent Neural Networks ........................................ 3.3.2 Time Series Analysis............................................................... 3.4 Functional Spaces............................ 3.4.1 Metric Space............................................................................. 39 39 40 41 42 43 43 44 45 46 50 53 53 ix
x Contents Vector Space........................................................................................ 3.5.1 NormedSpace........................................................................... 3.5.2 Hilbert Space............................................................................. 3.6 Questions................................................................................................ References....................................................................................................... 54 57 58 60 60 4 Autoencoder and GAN................................................................................ 4.1 Autoencoder........................................................................................... 4.2 Regularizatiohs and Autoencoders...................................................... 4.3 Generative Adversarial Networks................................ 4.4 Information Theory....................................... 4.5 Questions................................................................................................ References....................................................................................................... 65 65 66 68 71 75 76 5 Reinforcement Learning.............................................................................. 5.1 Introduction........................................................................................... 5.2 Bellman Equation................................................................................. 5.3 Deep
^-Learning................................................................................. 5.4 Optimization ........................................................................................ 5.5 Data Fitting........................................................................................... 5.6 Questions............................................................................................... References........................................................................ 77 77 78 80 83 83 86 87 6 CapsNet and Manifold Learning................................................................ 6.1 CapsNet.................................................................................................. 6.2 Manifold Learning............................................................................... 6.3 Questions............................................................................................... References....................................................................................................... 89 89 92 95 97 7 Boltzmann Machines..................... 7.1 Boltzmann Machine.................................................. 7.2 Restricted Boltzmann Machine.......................................................... 7.3 Deep Boltzmann Machine................................................................... 7.4 Probabilistic Graphical Models.......................................................... 7.5 Questions......................
References....................................................................................................... 99 99 99 102 102 107 107 8 Transfer Learning and Ensemble Learning........................................... 8.1 Transfer Learning................................................................................. 8.1.1 Transfer Learning.................................................................... 8.1.2 Taskonomy................................................................................ 8.2 Siamese Neural Networks................................................................... 8.3 Ensemble Learning............................................................................... 8.4 Important Work in Deep Learning..................................................... 109 109 109 110 Ill 112 115 3.5
Contents xi 8.5 Awarded Work in Deep Learning...................................................... 8.6 Questions. ............................................................................................. References...................................................................................................... 118 118 118 Glossary................................................................................................................ 121 Index..................................................................................................................... 127
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| adam_txt |
Contents 1 Introduction. 1.1 Introduction . 1.2 Deep Learning. 1.3 The Chronicle of Deep Learning. 1.4 Our Deep Learning Projects. 1.5 Awarded Work in Deep Learning. 1.6 Questions. References. 1 1 3 6 12 14 15 15 2 Deep Learning Platforms. 2.1 Introduction. 2.2 MATLAB for Deep Learning. 2.3 TensorFlow for Deep Learning. 2.4 Data Augmentation. 2.5 Fundamental Mathematics. 2.6 Questions. References. 21 21 22 26 31 32 36 37 3 CNN and
RNN. 3.1 CNN and YOLO. 3.1.1 R-CNN. 3.1.2 Mask R-CNN. 3.1.3 YOLO . 3.1.4 SSD. 3.1.5 DenseNets and ResNets. 3.2 RNN and Time Series Analysis. 3.3 HMM. 3.3.1 RNN: Recurrent Neural Networks . 3.3.2 Time Series Analysis. 3.4 Functional Spaces. 3.4.1 Metric Space. 39 39 40 41 42 43 43 44 45 46 50 53 53 ix
x Contents Vector Space. 3.5.1 NormedSpace. 3.5.2 Hilbert Space. 3.6 Questions. References. 54 57 58 60 60 4 Autoencoder and GAN. 4.1 Autoencoder. 4.2 Regularizatiohs and Autoencoders. 4.3 Generative Adversarial Networks. 4.4 Information Theory. 4.5 Questions. References. 65 65 66 68 71 75 76 5 Reinforcement Learning. 5.1 Introduction. 5.2 Bellman Equation. 5.3 Deep
^-Learning. 5.4 Optimization . 5.5 Data Fitting. 5.6 Questions. References. 77 77 78 80 83 83 86 87 6 CapsNet and Manifold Learning. 6.1 CapsNet. 6.2 Manifold Learning. 6.3 Questions. References. 89 89 92 95 97 7 Boltzmann Machines. 7.1 Boltzmann Machine. 7.2 Restricted Boltzmann Machine. 7.3 Deep Boltzmann Machine. 7.4 Probabilistic Graphical Models. 7.5 Questions.
References. 99 99 99 102 102 107 107 8 Transfer Learning and Ensemble Learning. 8.1 Transfer Learning. 8.1.1 Transfer Learning. 8.1.2 Taskonomy. 8.2 Siamese Neural Networks. 8.3 Ensemble Learning. 8.4 Important Work in Deep Learning. 109 109 109 110 Ill 112 115 3.5
Contents xi 8.5 Awarded Work in Deep Learning. 8.6 Questions. . References. 118 118 118 Glossary. 121 Index. 127 |
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| spelling | Yan, Wei Qi Verfasser (DE-588)1120154391 aut Computational methods for deep learning theoretic, practice and applications Wei Qi Yan Cham, Switzerland Springer [2021] © 2021 xvii, 134 Seiten Illustrationen, Diagramme, 1 Karte (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Texts in computer science Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Mathematics of Computing Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks Optical data processing Machine learning Computer science—Mathematics Artificial intelligence Neural networks (Computer science) Optische Datenverarbeitung (DE-588)4172661-3 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Mustererkennung (DE-588)4040936-3 s Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s Optische Datenverarbeitung (DE-588)4172661-3 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-61081-4 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033022682&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Yan, Wei Qi Computational methods for deep learning theoretic, practice and applications Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Mathematics of Computing Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks Optical data processing Machine learning Computer science—Mathematics Artificial intelligence Neural networks (Computer science) Optische Datenverarbeitung (DE-588)4172661-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Mustererkennung (DE-588)4040936-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
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| title | Computational methods for deep learning theoretic, practice and applications |
| title_auth | Computational methods for deep learning theoretic, practice and applications |
| title_exact_search | Computational methods for deep learning theoretic, practice and applications |
| title_exact_search_txtP | Computational methods for deep learning theoretic, practice and applications |
| title_full | Computational methods for deep learning theoretic, practice and applications Wei Qi Yan |
| title_fullStr | Computational methods for deep learning theoretic, practice and applications Wei Qi Yan |
| title_full_unstemmed | Computational methods for deep learning theoretic, practice and applications Wei Qi Yan |
| title_short | Computational methods for deep learning |
| title_sort | computational methods for deep learning theoretic practice and applications |
| title_sub | theoretic, practice and applications |
| topic | Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Mathematics of Computing Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks Optical data processing Machine learning Computer science—Mathematics Artificial intelligence Neural networks (Computer science) Optische Datenverarbeitung (DE-588)4172661-3 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Mustererkennung (DE-588)4040936-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Maschinelles Sehen (DE-588)4129594-8 gnd |
| topic_facet | Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Mathematics of Computing Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks Optical data processing Machine learning Computer science—Mathematics Artificial intelligence Neural networks (Computer science) Optische Datenverarbeitung Künstliche Intelligenz Mustererkennung Maschinelles Lernen Maschinelles Sehen |
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