Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/64465
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dc.contributor.advisorVitrià i Marca, Jordi-
dc.contributor.authorBrando Guillaumes, Axel-
dc.date.accessioned2015-03-24T09:15:01Z-
dc.date.available2015-03-24T09:15:01Z-
dc.date.issued2015-01-23-
dc.identifier.urihttp://hdl.handle.net/2445/64465-
dc.descriptionTreballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2015, Director: Jordi Vitrià i Marcaca
dc.description.abstractThe deep learning techniques and the use of GPUs have made neural networks the leading option for solving some computational problems and it has been shown to produce the state-of-the-art results in many fields like computer vision, automatic speech recognition, natural language processing, and audio recognition. This final grade dissertation is divided into three parts: first, we will discuss theoretical concepts that allow us to understand how neural networks work. Second, we will focus in deep convolutional neural network to understand and learn how to build such networks that Caffe Framework use. Finally, the third part will be a compilation of all learned skills to make that a neural network will classify correctly the data set MNIST, and then we will change Caffe Framework files so it can read images with more channels and we will watch the results obtained. At last, we will match and improve the public state-of-the-art classification system of the data set Food-101. After all the work, our goals will be achieved: we will modify Caffe Framework and we will check that in the case of the MNIST we will improves the classification rate. But above all, the result that we want to emphasize is the to release of classification system for the Food-101 that will improve the accuracy from 56.40% to 74.6834% and finally we will propose ideas for improving this classification in the future.ca
dc.format.extent57 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isocatca
dc.rightsmemòria: cc-by-nc-sa (c) Axel Brando Guillaumes, 2015-
dc.rightscodi: GPL (c) Axel Brando Guillaumes, 2015-
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/es-
dc.rights.urihttp://www.gnu.org/licenses/gpl-3.0.ca.html-
dc.subject.classificationXarxes neuronals (Informàtica)cat
dc.subject.classificationAprenentatge automàticcat
dc.subject.classificationProgramaricat
dc.subject.classificationTreballs de fi de graucat
dc.subject.classificationIntel·ligència artificialca
dc.subject.otherNeural networks (Computer science)eng
dc.subject.otherMachine learningeng
dc.subject.otherComputer softwareeng
dc.subject.otherBachelor's thesiseng
dc.subject.otherArtificial intelligencees
dc.titleEstudi de les xarxes neuronals convolucionals profundes mitjançant Caffeca
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Programari - Treballs de l'alumnat
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

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