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master's thesis
Učenje podrškom u strojnom učenju
2019. urn:nbn:hr:166:010889
Jerković, Ivana
University of Split
Faculty of Science
Department of Mathematics

Cite this document

Jerković, I. (2019). Učenje podrškom u strojnom učenju (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:010889

Jerković, Ivana. "Učenje podrškom u strojnom učenju." Master's thesis, University of Split, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:166:010889

Jerković, Ivana. "Učenje podrškom u strojnom učenju." Master's thesis, University of Split, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:166:010889

Jerković, I. (2019). 'Učenje podrškom u strojnom učenju', Master's thesis, University of Split, Faculty of Science, accessed 15 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:010889

Jerković I. Učenje podrškom u strojnom učenju [Master's thesis]. Split: University of Split, Faculty of Science; 2019 [cited 2024 November 15] Available at: https://urn.nsk.hr/urn:nbn:hr:166:010889

I. Jerković, "Učenje podrškom u strojnom učenju", Master's thesis, University of Split, Faculty of Science, Split, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:166:010889

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Metadata
TitleUčenje podrškom u strojnom učenju
Title (english)Reinforcement Learning in Machine Learning
AuthorIvana Jerković
MentorIvo Ugrina (mentor)
Committee memberIvo Ugrina (predsjednik povjerenstva)
Committee memberJurica Perić (član povjerenstva)
Committee memberMilica Klaričić Bakula (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Mathematics)
Split
Defense date and country2019-06-21, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
Cilj je ovoga diplomskog rada objasniti učenje podrškom - paradigmu učenja neuronskih mreža koja se zasniva na metodi pokušaja i pogrešaka. U tu je svrhu prvo opisan način rada neuronskih mreža te osnovni elementi istih. Definiran je i Markovljev proces odlučivanja koji je osnova algoritama Q-učenja koji se koriste za rješavanje problema metodom učenja podrškom te su opisani algoritmi Q-učenja s iteracijama vrijednosti i dubinskog Q-učenja. Na kraju je dan primjer implementacije dubinskog Q-učenja u programskom jeziku Python.
Abstract (english)
The goal of this master’s thesis is to explain reinforcement learning - neural network learning method based on trial and error principle. For that purpose, the way that neural networks work and their basic elements are first described. Furthermore, Markov decision process, which is the basis of Q-learning algorithms that are being used to solve problems with reinforcement learning, is defined and algorithms of Q-learning with value iterations and deep Q-learning are described. An example of implementation of deep Q-learning written in Python programming language is given at the end.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:010889
Study programmeTitle: Mathematics; specializations in: major in pure mathematics, major in computing, major in teaching
Course: major in computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2020-06-05 11:40:31