20.500.12556/RUL-140493 Konvolucijske nevronske mreže in njihova uporaba delo diplomskega seminarja Convolutional neural networks and their applications Konvolucijske nevronske mreže so vrsta nevronskih mrež, te pa spadajo pod metode strojnega učenja. Strojno učenje je vrsta umetne inteligence, kamor uvrščamo modele in algoritme za napovedovanje in analizo podatkov. Primarni namen konvolucijskih nevronskih mrež je analiziranje vizualnih podob, kot so na primer slike in video podatki. Z njihovo uporabo lahko prepoznavamo poljubne lastnosti in vzorce. Zgradba konvolucijskih nevronskih mrež je podobna splošnim nevronskim mrežam, z razliko od ekaterih specifičnih plasti. Vsako konvolucijsko nevronsko mrežo sestavljajo konvolucijska plast, ReLU plast (ali katera druga aktivacijska plast), združevalna plast, popolno povezana plast in običajno tudi softmax plast. V delu je predstavljena matematična izpeljava delovanja in struktura ob učenju modela. Konvolucijske nevronske mreže so čedalje bolj popularne in se jih uporablja na številnih področjih. Eno od področij, so finančnih trgi. V delu je podan primer uporabe konvolucijskih nevronskih mrež za napovedovanje gibanja finančnega indeksa. Convolutional neural networks are a type of neural networks, which fall under the category of machine learning methods. Machine learning is a type of artificial intelligence that includes models and algorithms for prediction and data analysis. The primary purpose of convolutional neural networks is to analyze visual images such as pictures and videos. With their help, we can recognize arbitrary features and patterns. Their structure is similar to general neural networks, with the difference of some specific layers. Each convolutional neural network consists of a convolutional layer, a ReLU layer (or some other activation layer), a pooling layer, a fully connected layer, and usually a softmax layer. This work presents the mathematical derivation of the operation and structure during model learning. Convolutional neural networks are becoming more and more popular and are being used in many areas. One of them is a financial market. For this purpuse an example of financial time-series data analysis using convolutional neural networks is described. nevronske mreže konvolucijske nevronske mreže strojno učenje konvolucijska plast ReLU plast združevalna plast gradientni spust neural networks convolutional neural networks machine learning convolutional layer ReLU layer pooling layer gradient descent true false false Slovenski jezik Angleški jezik Delo diplomskega seminarja/zaključno seminarsko delo/naloga 2022-09-15 08:15:14 2022-09-15 08:15:15 2024-05-29 12:11:27 0000-00-00 00:00:00 2022 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 127134 122087171 5431.pdf 5431.pdf 1 38628FD4C1D40D7B2CBD4F5CF0052B6D b64ee5a040ecdc36a764acacfe70d25bc9a2b4acc69c1da777338495d2293829 b2edf63b-34bd-11ed-92af-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=161219 Fakulteta za matematiko in fiziko 0 0 0