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undergraduate thesis
Učenje konvolucijskih modela u miješanoj točnosti
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:431599
Oreč, Dario
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Oreč, D. (2021). Učenje konvolucijskih modela u miješanoj točnosti (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:431599

Oreč, Dario. "Učenje konvolucijskih modela u miješanoj točnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:431599

Oreč, Dario. "Učenje konvolucijskih modela u miješanoj točnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:431599

Oreč, D. (2021). 'Učenje konvolucijskih modela u miješanoj točnosti', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:431599

Oreč D. Učenje konvolucijskih modela u miješanoj točnosti [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:431599

D. Oreč, "Učenje konvolucijskih modela u miješanoj točnosti", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:431599

Metadata
TitleUčenje konvolucijskih modela u miješanoj točnosti
Title (english)Mixed-precision training of convolutional models
AuthorDario Oreč
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberJosip Šarić (član povjerenstva)
Committee memberIvan Grubišić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu su opisane umjetne neuronske mreže, a posebice konvolucijske neuronske mreže. Opisane su metode učenja pomoću gradijentnog spusta i propagacije pogreške unazad, kao i neke tehnike regularizacije. Također je pobliže obrađen rad i korištenje miješane preciznosti, njene prednosti u odnosu na tradicionalni pristup i implementacija u radnom okviru PyTorch. Implementirani su i evaluirani modeli koji koriste miješanu preciznost i oni koji ju ne koriste. Modeli su implementirani pomoću rezidualnih neuronskih mreža, ResNet-18, ResNet-34 i ResNet-50. Na kraju rada su prikazani i uspoređeni rezultati tradicionalnog pristupa i pristupa koji koristi miješanu preciznost.
Abstract (english)
Paper describes artificial neural networks, and in particular convolutional neural networks. Gradient descent learning methods and back-propagation technique are described, as well as some regularization techniques. The work and use of mixed precision, its advantages over traditional approach, and implementation in the PyTorch framework are also discussed in more detail. Models that use mixed precision and those that do not use it have been implemented and evaluated. The models were implemented using residual neural networks, ResNet-18, ResNet-34 i ResNet-50. At the end of the paper, the results of the traditional approach and the approach that uses mixed precision are presented and compared.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:431599
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 21:19:48