Thesis Details

Hluboké neuronové sítě v rozpoznávání obrazu

Bachelor's Thesis Student: Kozel Michal Academic Year: 2014/2015 Supervisor: Hradiš Michal, Ing., Ph.D.
English title
Deep Learning for Image Recognition
Language
Czech
Abstract

Neural networks are currently state-of-the-art technology for speech, image and other recognition tasks. This thesis describes basis properties of neural networks and their learning. The aim of this thesis was to extend Caffe framework with new learning methods and compare their performance on Cifar10 dataset. Namely RMSPROP and normalized SGD

Keywords

Neural networks, deep learning, convolutional neural networks, image recognition, Cifar-10, RMSPROP, normalized SGD

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade D
Date
15 June 2015
Reviewer
Španěl Michal, Ing., Ph.D.
Committee
Smrž Pavel, doc. RNDr., Ph.D. (DCGM FIT BUT), předseda
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Orság Filip, Ing., Ph.D. (DITS FIT BUT), člen
Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Citation
KOZEL, Michal. Hluboké neuronové sítě v rozpoznávání obrazu. Brno, 2015. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2015-06-15. Supervised by Hradiš Michal. Available from: https://www.fit.vut.cz/study/thesis/17157/
BibTeX 
@bachelorsthesis{FITBT17157,
    author = "Michal Kozel",
    type = "Bachelor's thesis",
    title = "Hlubok\'{e} neuronov\'{e} s\'{i}t\v{e} v rozpozn\'{a}v\'{a}n\'{i} obrazu",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2015,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/17157/"
}

Theses

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