Nevronske mreže z vzvratnim razširjanjem napak v funkcijskem programskem jeziku : delo diplomskega seminarja

Guzelj Blatnik, Laura (author), Todorovski, Ljupčo (mentor), Osojnik, Aljaž (comentor)

Diplomsko delo se poglobi v usmerjene nevronske mreže. Le te temeljijo na posnemanju možganskih funkcij, uporabljajo pa se za napovedovanje in klasifikacijo. Sestavljajo jih nevroni organizirani v sloje. Nevroni so med sabo povezani s sinapsami. Usmerjene nevronske mreže iz stanja nevronov v vhodnem sloju preko uteži na sinapsah in nevronih v vmesnih slojih, izračunajo napoved v izhodnem sloju. S pomočjo algoritma za vzvratno razširjanje napake in učnih primerov mrežo naučimo odzivanja na neznane situacije. Algoritem temelji na spreminjanju uteži na sinapsah, učenje pa poteka dokler ni razlika med želeno in izračunano vrednostjo dovolj majhna. Poleg nevronskih mrež se delo osredotoči tudi na funkcijsko programiranje s poudarkom na programskem jeziku OCaml. Primer nevronske mreže z vzvratnim razširjanjem napak je tudi implementiran v programskem jeziku OCaml. Delovanje mreže je prikazano na konkretnem primeru, kjer je ovrednotena napaka mreže na izbrani podatkovni množici.

• Language: Slovenian
• Keywords: usmerjene nevronske mreže, vzvratno razširjanje napake, perceptron, umetna inteligenca, strojno učenje, funkcijski programski jezik OCaml
• Work type: Final seminar paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FMF - Faculty of Mathematics and Physics
• Year: 2020
• PID: 20.500.12556/RUL-117661
• UDC: 004.8
• COBISS.SI-ID: 58750723
• Publication date in RUL: 19.07.2020

GUZELJ BLATNIK, Laura, 2020, Nevronske mreže z vzvratnim razširjanjem napak v funkcijskem programskem jeziku : delo diplomskega seminarja [online]. Bachelor’s thesis. [Accessed 31 May 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=117661

Secondary language

• Language: English
• Title: Feed-forward neural networks with backpropragtion in a functional programming language

Abstract

This paper focuses on feed-forward neural networks. Mimicking brain functions, neural networks are used for prediction and classification. Neural networks are composed of neurons organized in layers and connected by synapses. From the input values through weights on the synapses and neurons in hidden layers, neural networks compute the prediction in the output layer. Utilizing the backpropagation algorithm and learning examples the network is able to learn how to respond to unknown situations. The idea behind backpropagation is to change weights on the synapses until the difference between computed and desired values is small enough. In addition, the work presents functional programming and the OCaml functional programming language. An implementation of a neural network with backpropagation in the OCaml programming language is presented at the end of the work. The work concludes with an application of the neural network to a real-world example, where the error of the network is evaluated on a specific data set.

• Keywords: feed-forward neural network, backpropagation, perceptron, artificial intelligence, machine learning, functional programming language OCaml