Deep learning neural networks : Design and case studies / Daniel Graupe
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9780000988546
- 23 006.31 GRA
Contents:
1. Deep learning neural networks: methodology and scope
2. Basic concepts of neural networks
3. Back-propagation
4. The cognitron and neocognitron
5. Deep learning convolutional neural networks
6. LAMSTAR-1 and LAMSTAR-2 neural networks
7. Other neural networks for deep learning
8. Case studies
9. Concluding comments
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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CUTN Central Library Generalia | Non-fiction | 006.31 GRA (Browse shelf(Opens below)) | Available | 46865 |
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006.31 GOO Deep learning / | 006.31 GOO Deep learning / | 006.31 GOO Deep learning / | 006.31 GRA Deep learning neural networks : Design and case studies / | 006.31 JOS Adversarial machine learning / | 006.31 KAR Machine learning for Engineers . | 006.31 LIU Computational trust models and machine learning / |
1. Deep learning neural networks: methodology and scope
2. Basic concepts of neural networks
3. Back-propagation
4. The cognitron and neocognitron
5. Deep learning convolutional neural networks
6. LAMSTAR-1 and LAMSTAR-2 neural networks
7. Other neural networks for deep learning
8. Case studies
9. Concluding comments
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