75 research outputs found

    Model, automaat, voorspelling

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    Desk top simulation environments.

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    An ICT strategy for Europe.

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    Review of the AECL Post Closure Assessment and related documents.

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    Generativne kontradikcijske neuronske mreže jedan su od najuspješnijih generativnih modela strojnog učenja. Njihovo izvođenje se temelji na natjecanju dviju neuronskih mreža u hipotetskoj igri s nultom sumom. Za razliku od mnogih drugih generativnih modela, GEKON mreže ne koriste funkcije gustoće vjerojatnosti kako bi opisale distribuciju vjerojatnosti već generiraju nove vrijednosti isključivo na temelju proučavanja značajki na velikim bazama podataka. U ovom radu prikazana je temeljna teorijska i matematička pozadina neuronskih mreža, nakon čega je detaljnije opisana struktura GEKON modela i mogućnost njegove optimizacije za specifične zadatke. U radu su također prikazani primjeri uporabe takvog modela s naglaskom na primjenu u robotici, ali i etička pitanja koja nastaju zbog iznimno realističnih rezultata takvih mreža. Na kraju rada prikazana je i usporedba implementacije triju vrsta GEKON modela za generiranje realističnih „rukom napisanih“ znamenki.Generative adversarial networks are one of the most successful generative models of machine learning. Their performance is based on the competition of two neural networks in a hypothetical zero-sum game. Unlike many other generative models, GANs do not use probability density functions to describe the probability distribution, but rather generate new values solely based on the study of features on large databases. This paper presents the basic theoretical and mathematical background of neural networks, after which the structure of GANs and the possibilities of its optimization for specific tasks are described in more detail. The paper also presents examples of using such a model with an emphasis on the applications in robotics, but also ethical issues that arise due to the extremely realistic results of such networks. A comparison of the implementation of three types of GANs for generating realistic "handwritten" digits is presented at the end of the paper

    Review of the AECL Post Closure Assessment and related documents.

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