| Titre : | Deep Learning Neural Networks : Design and Case Studies |
| Auteurs : | Daniel Graupe, Auteur |
| Type de document : | document électronique |
| Editeur : | New Jersey : World Scientific, 2016 |
| ISBN/ISSN/EAN : | 978-981-314-646-4 |
| Format : | 1 vol. (280 p.) / ill. couv. en coul / PDF |
| Langues: | Anglais |
| Index. décimale : | 006.32 (Réseaux neuronaux) |
| Catégories : |
Ouvrages > Généralités (ouvrages généraux), information, informatique > Informatique |
| Mots-clés: | Neural Networks=Réseaux neuronaux |
| Résumé : |
Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance. This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research. |
| Note de contenu : |
Contents: Chapter 1: Deep Learning Neural Networks — Methodology and Scope Chapter 2: Basic Concepts in Neural Networks Chapter 3: Back-Propagation Chapter 4: The Cognitron and Neocognitron Chapter 5: Deep Learning Convolutional Neural Network Chapter 6: LAMSTAR-1 and LAMSTAR-2 Neural Networks Chapter 7: Other Neural Networks for Deep Learning Chapter 8: Case Studies Chapter 9: Concluding Comments |
| Côte titre : | F8/12880 |
Exemplaires (1)
| Cote | Support | Localisation | Disponibilité |
|---|---|---|---|
| F8/12880 | Ebook | Faculté de Technologie | Disponible |
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