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undergraduate thesis
Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža
Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2022. urn:nbn:hr:200:013019
Hofer, Filip
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

Institutional repository: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

Cite this document

Hofer, F. (2022). Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:013019

Hofer, Filip. "Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2022. https://urn.nsk.hr/urn:nbn:hr:200:013019

Hofer, Filip. "Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2022. https://urn.nsk.hr/urn:nbn:hr:200:013019

Hofer, F. (2022). 'Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:200:013019

Hofer F. Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2022 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:200:013019

F. Hofer, "Klasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:200:013019

Metadata
TitleKlasificiranje grafičkih prikaza korištenjem plitkih neuronskih mreža
Title (english)Chart Image Classification Using Shallow Convolutional Neural Networks
AuthorFilip Hofer
MentorJosip Job (mentor)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2022-09-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Electronics
Abstract
Duboke neuronske mreže su standardni alat koji pomaže u rješavanju problema kao što su detekcija objekata, raspoznavanje oblika i klasificiranje podataka. Potrebna je određena količina znanja kako bi se duboka neuronska mreže istrenirala za željenu radnju. Precizno označeni podatkovni skupovi uvelike pomažu pri učenju neuronske mreže. Što više podataka za učenje proslijedimo neuronskoj mreži to će ona bolje raditi svoj zadatak za koji je istrenirana. U ovom radu opisane su osnove umjetne inteligencije i strojnog učenja. Istražene su vrste neuronskih mreža i razlika između dubokih i plitkih neuronskih mreža. U svrhu istraživanja razlike učinkovitosti između plitkih i dubokih konvolucijskih neuronskih mreža odabrana je mala konvolucijska neuronska mreža koja se sastoji od dva konvolucijska sloja te tri potpuno povezana sloja i VGG-11 konvolucijska neuronska mreža koja se sastoji od osam konvolucijskih i tri potpuno povezana sloja.
Abstract (english)
Deep neural networks are a standard tool to help solve problems such as object detection, shape recognition, and data classification. A certain amount of knowledge is required to train a deep neural network for a desired action. Precisely labeled datasets greatly aid neural network learning. The more training data we feed the neural network, the better it will perform at the task for which it was researched. This paper describes the basics of artificial intelligence and machine learning. The types of neural networks and the difference between deep and shallow neural networks are explored. In order to investigate the difference in efficiency between shallow and deep convolutional neural networks, a small convolutional neural network consisting of two convolutional layers and three fully connected layers and a VGG-11 convolutional neural network consisting of eight convolutional and three fully connected layers were selected.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:013019
Study programmeTitle: Undergraduate University Study Programme
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka elektrotehnike (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
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Created on2023-01-02 12:52:37