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undergraduate thesis
Analiza rada VGG arhitekture neuronske mreže
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:235:078686
Trbara, Klara
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Trbara, K. (2022). Analiza rada VGG arhitekture neuronske mreže (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:078686

Trbara, Klara. "Analiza rada VGG arhitekture neuronske mreže." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:078686

Trbara, Klara. "Analiza rada VGG arhitekture neuronske mreže." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:078686

Trbara, K. (2022). 'Analiza rada VGG arhitekture neuronske mreže', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 13 July 2024, https://urn.nsk.hr/urn:nbn:hr:235:078686

Trbara K. Analiza rada VGG arhitekture neuronske mreže [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2022 [cited 2024 July 13] Available at: https://urn.nsk.hr/urn:nbn:hr:235:078686

K. Trbara, "Analiza rada VGG arhitekture neuronske mreže", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:235:078686

Metadata
TitleAnaliza rada VGG arhitekture neuronske mreže
Title (english)VGG neural network architecture performance analysis
AuthorKlara Trbara
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2022-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
Kroz ovaj rad je prikazan razvoj konvolucijske neuronske mreže primjenjujući VGG arhitekturu u problemu rješavanja klasifikacije slika. Slike pasa i mačaka su zadane iz poznate baze podataka zadane u svrhu Kaggle-ovog natjecanja iz strojnog učenja iz 2013. godine. U uvodu rada se upoznajemo s osnovnom gradivnom jedinicom neuronske mreže perceptorom i osnovnom principima njezinog rada. U nastavku će se predstaviti opća arhitektura konvolucijskih neuronskih mreža. Naglasak ovog rada je na savladavanju principa razvoja osnovnog modela VGG arhitekture neuronske mreže s kojom se želi prikazati rast preciznosti klasifikacije mreže dodavanjem novih blokova. Prikazat će se razvoj osnovnog modela kroz korištenje cloud servisa i online sučelja Google Colab koristeći se otvorenim softverskim bibliotekama za strojno učenje (Tenserflow). Potom se na istoj bazi podataka testira već unaprijed istrenirana arhitektura neuronske mreže VGG-16 te se uspoređuju rezultati s osnovnim modelom.
Abstract (english)
This paper presents the development of a convolutional neural network applying the VGG architecture to the problem of image classification. The images of dogs and cats were taken from a well-known database provided for Kaggle's 2013 machine learning competition. In first part of the paper, we are introduced to the basic building block of the neural network, the preceptor, and the basic principles of its operation. Furthermore, the general architecture of convolutional neural networks will be presented. The emphasis of this paper is on mastering the principles of developing the basic model of the VGG architecture of the neural network, with which we want to show the growth of the accuracy of the network classification by stacking new blocks. The development of the basic model will be presented through the use of cloud services and the Google Colab online interface using open software libraries for machine learning (TensorFlow). Then, the pre-trained VGG-16 neural network architecture is tested on the same database and the results are compared with the basic model.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:078686
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-09-26 11:13:02