Deep Learning
| dc.contributor.author | Schulz, Hannes | |
| dc.contributor.author | Behnke, Sven | |
| dc.date.accessioned | 2018-01-08T09:16:10Z | |
| dc.date.available | 2018-01-08T09:16:10Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Hierarchical neural networks for object recognition have a long history. In recent years, novel methods for incrementally learning a hierarchy of features from unlabeled inputs were proposed as good starting point for supervised training. These deep learning methods—together with the advances of parallel computers—made it possible to successfully attack problems that were not practical before, in terms of depth and input size. In this article, we introduce the reader to the basic concepts of deep learning, discuss selected methods in detail, and present application examples from computer vision and speech recognition. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11316 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Hierarchical feature learning | |
| dc.subject | Object categorization | |
| dc.subject | Unsupervised learning | |
| dc.title | Deep Learning | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 363 | |
| gi.citation.startPage | 357 |