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Convolution neural networks on the Windows platform
Kapusta, Martin ; Rajnoha, Martin (oponent) ; Přinosil, Jiří (vedoucí práce)
The aim of the bachelor thesis is the latest knowledge of convolution neural networks and their application. The thesis describes the history, biological neuron and analogous mathematical model of a neuron. It also deals with the areas where neural networks are used, as well as the areas in which they expand gradually, the ways of learning and training, the differences between convolution neural networks and classical neural networks and their architecture. The thesis consists of two parts. The first part is the selection of the framework for working with convolution neural networks, which is suitable for implementation in the Windows operating system, the installation of the framework and its troubleshooting. The second part is aimed at creating an automated installation tool for the Windows 7 and Windows 10 operating system, created in JavaFX.
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HelenOS installer
Táborský, Dominik ; Děcký, Martin (vedoucí práce) ; Yaghob, Jakub (oponent)
Schopnost sebe sama nainstalovat na trvalé úložiště je jedna z věcí defi- nujících použitelnost systému. V této práci se podíváme na naše možnosti jak toho dosáhnout v případě operačního systému HelenOS. Budeme se zabývat tím, jaké máme volby, jaké jsou jejich výhody a nevýhody a konečně jaké jsou jejich implementační detaily. Součástí práce je též prototypová implementace kritických částí, která je také popsána. Rozhodnutí při návrhu implementace jsou taktéž diskutována. 1
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Convolution neural networks on the Windows platform
Kapusta, Martin ; Rajnoha, Martin (oponent) ; Přinosil, Jiří (vedoucí práce)
The aim of the bachelor thesis is the latest knowledge of convolution neural networks and their application. The thesis describes the history, biological neuron and analogous mathematical model of a neuron. It also deals with the areas where neural networks are used, as well as the areas in which they expand gradually, the ways of learning and training, the differences between convolution neural networks and classical neural networks and their architecture. The thesis consists of two parts. The first part is the selection of the framework for working with convolution neural networks, which is suitable for implementation in the Windows operating system, the installation of the framework and its troubleshooting. The second part is aimed at creating an automated installation tool for the Windows 7 and Windows 10 operating system, created in JavaFX.
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