20.500.12556/DKUM-71179 Napovedovanje GPS sledi z globokimi nevronskimi mrežami Predicting GPS tracks with deep neural networks 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. 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. Strojno učenje globoko učenje globoke nevronske mreže povratne nevronske mreže. Machine learning deep learning deep neural networks recurrent neural networks. true true false [J. Borlinić] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2018-07-23 13:37:10 2018-12-13 14:10:49 2022-08-01 20:28:03 2019-07-11 18:33:02 2018 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85(043.2) 24225288 URN:SI:UM:DK:YKANTDCK MAG_Borlinic_Jernej_2018.pdf MAG_Borlinic_Jernej_2018.pdf 1 B1D100895E1877A799A656515BD4739A 6195efccb2399dad1d1ff7cdbebd1939fff9e91519465013d40473acb524596a 46b01c34-7c10-11eb-bb7a-00155d0001ca 20.500.12556/dkum/253999be-43f2-423a-92f9-e53687c566aa https://dk.um.si/Dokument.php?lang=slv&id=128939 Fakulteta za naravoslovje in matematiko 0 0 0