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Title:Napovedovanje GPS sledi z globokimi nevronskimi mrežami
Authors:ID Borlinić, Jernej (Author)
ID Taranenko, Andrej (Mentor) More about this mentor... New window
ID Orbanić, Alen (Comentor)
Files:.pdf MAG_Borlinic_Jernej_2018.pdf (9,58 MB)
MD5: B1D100895E1877A799A656515BD4739A
PID: 20.500.12556/dkum/253999be-43f2-423a-92f9-e53687c566aa
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:Metode strojnega učenja vse bolj prodirajo v vsa področja modernega gospodarskega in raziskovalnega okolja. Obstoječi algoritmi dosegajo vrhunske rezultate pri nalogah kot so prepoznavanje slik, razumevanje besedil in govora ipd. Avtomatizirane rešitve takšnih nalog so še nedavno veljale za nedosegljive. V tej magistrski nalogi pregledamo najpopularnejše globoke nevronske mreže, iz njih sestavljene modele in njihove načine učenja. S pridobljenim znanjem in večkratnim testiranjem v drugem delu, razvijemo model globoke nevronske mreže za napovedovanje GPS sledi. Osnovno testiranje modela poteka na lastnem naboru sintetično ustvarjenih podatkov. Dva najuspešnejša modela v nadaljevanju učimo s pomočjo izbranih realnih podatkov pridobljenih od podjetja GoOpti d. o. o. Končni izpopolnjen model pa učimo z razširjenim naborom realnih podatkov. V magistrski nalogi so opisani izbira in implementacija modela, način učenja, ustvarjanje in pridobivanje naborov podatkov in pridobljeni rezultati.
Keywords:Strojno učenje, globoko učenje, globoke nevronske mreže, povratne nevronske mreže.
Place of publishing:Maribor
Publisher:[J. Borlinić]
Year of publishing:2018
PID:20.500.12556/DKUM-71179 New window
UDC:004.85(043.2)
COBISS.SI-ID:24225288 New window
NUK URN:URN:SI:UM:DK:YKANTDCK
Publication date in DKUM:13.12.2018
Views:2515
Downloads:148
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:FNM
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:23.07.2018

Secondary language

Language:English
Title:Predicting GPS tracks with deep neural networks
Abstract:Machine learning methods are increasingly influencing all areas of the modern economic and research environment. Existing algorithms achieve top results in tasks such as image recognition, understanding text and speech, etc. Automated solutions to such tasks were until recently considered unavailable. In this master's thesis, we review the most popular deep neural networks, underlying models and their learning tipes. With the acquired knowledge and repeated testing in the second part, we develop a deep neural network model for predicting GPS tracks. Basic testing of the model takes place on our own synthetically generated dataset. The two most successful models are further taught using selected real data obtained from GoOpti d. o. o. and the final, best performing, model is taught with an expanded set of real data. The master's thesis describes the choice and implementation of the model, the tipe of learning, the creation and retrieval of data sets, and the obtained results.
Keywords:Machine learning, deep learning, deep neural networks, recurrent neural networks.


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