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Deep learning review and its applications
dc.contributor.advisor | D'Anjou, Alicia | |
dc.contributor.author | Azkarate Saiz, Andoni | |
dc.contributor.other | Ciencia de la Computación e Inteligencia Artificial/Konputazio Zientzia eta Adimen Artifiziala | |
dc.date.accessioned | 2015-10-08T09:25:02Z | |
dc.date.available | 2015-10-08T09:25:02Z | |
dc.date.issued | 2015-10-08 | |
dc.identifier.uri | http://hdl.handle.net/10810/15792 | |
dc.description.abstract | Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed. | es |
dc.language.iso | eng | es |
dc.relation.ispartofseries | 2015;1 | |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | deep learning | es |
dc.subject | machine learning | es |
dc.subject | artificial neural network | es |
dc.subject | visual recognition | es |
dc.subject | object recognition | es |
dc.subject | object mining | es |
dc.subject | pattern recognition | es |
dc.subject | computer vision | es |
dc.subject | convolutional neural network | es |
dc.subject | ca e | es |
dc.title | Deep learning review and its applications | es |
dc.type | info:eu-repo/semantics/masterThesis | es |
dc.rights.holder | Attribution-NonCommercial-ShareAlike 4.0 International | * |