Principles of artificial neural networks: basic designs to deep learning
Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their structures -- The perceptron -- The madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The cognitron and neoco...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New Jersey ; London ; Singapore
World Scientific
[2019]
|
| Edition: | 4th edition |
| Series: | Advanced series in circuits and systems
Vol. 8 |
| Subjects: | |
| Online Access: | FHI01 TUM01 TUM02 UBY01 FHN01 Volltext Inhaltsverzeichnis |
| Summary: | Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their structures -- The perceptron -- The madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The cognitron and neocognition -- Statistical training -- Recurrent (time cycling) back propagation networks -- Deep learning neural networks : principles and scope -- Deep learning convolutional neural network -- LAMSTAR neural networks -- Performance of DLNN : comparative case studies |
| Physical Description: | 1 Online-Ressource (xvi, 422 Seiten) |
| ISBN: | 9789811201233 9789811201240 |
| DOI: | 10.1142/11306 |
Staff View
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| spelling | Graupe, Daniel Verfasser (DE-588)1129263053 aut Principles of artificial neural networks basic designs to deep learning Daniel Graupe, University of Illinois, Chicago, USA 4th edition New Jersey ; London ; Singapore World Scientific [2019] 1 Online-Ressource (xvi, 422 Seiten) txt rdacontent c rdamedia cr rdacarrier Advanced series in circuits and systems Vol. 8 Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their structures -- The perceptron -- The madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The cognitron and neocognition -- Statistical training -- Recurrent (time cycling) back propagation networks -- Deep learning neural networks : principles and scope -- Deep learning convolutional neural network -- LAMSTAR neural networks -- Performance of DLNN : comparative case studies Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neural networks (Computer science) COMPUTERS / General Electronic books Neuronales Netz (DE-588)4226127-2 s DE-604 Erscheint auch als Druck-Ausgabe, Hardcover 978-981-12-0122-6 https://doi.org/10.1142/11306 Verlag URL des Erstveröffentlichers Volltext DE-601 pdf/application https://www.gbv.de/dms/bowker/toc/9789811201226.pdf 2020-01-19 Aggregator Inhaltsverzeichnis |
| spellingShingle | Graupe, Daniel Principles of artificial neural networks basic designs to deep learning Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4226127-2 |
| title | Principles of artificial neural networks basic designs to deep learning |
| title_auth | Principles of artificial neural networks basic designs to deep learning |
| title_exact_search | Principles of artificial neural networks basic designs to deep learning |
| title_exact_search_txtP | Principles of artificial neural networks basic designs to deep learning |
| title_full | Principles of artificial neural networks basic designs to deep learning Daniel Graupe, University of Illinois, Chicago, USA |
| title_fullStr | Principles of artificial neural networks basic designs to deep learning Daniel Graupe, University of Illinois, Chicago, USA |
| title_full_unstemmed | Principles of artificial neural networks basic designs to deep learning Daniel Graupe, University of Illinois, Chicago, USA |
| title_short | Principles of artificial neural networks |
| title_sort | principles of artificial neural networks basic designs to deep learning |
| title_sub | basic designs to deep learning |
| topic | Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Neuronales Netz |
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