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	Deep Neural Network Pruning for Text Recognition Petráš, Simon ; Hradiš, Michal (referee) ; Kišš, Martin (advisor) This document is a work on pruning neural network for handwriting recognition. The aim of the work is to create a program for pruning the network. We prune two types of neural networks, namely convolutional and recurrent neural networks. During the pruning of the convolution part, various criteria of parameter selection were experimented with. The result of the work is a model that achieves 20% acceleration while increasing the network inaccuracy by only 0.4%, but also a number of other models that are faster but also acquire higher inaccuracies. Detailed record
	Model Compression of Denoising Diffusion Probabilistic Models for Image Generation Dobiš, Lukáš ; Kišš, Martin (referee) ; Hradiš, Michal (advisor) Táto diplomová práca sa zameriava na optimalizáciu výpočtovej efektívnosti generatívnych difúznych modelov skrz vyhodnotenie konvenčných metód komprimácie neurónovych sieti na architektúre Denoising Diffusion Probabilistic Model (DDPM). Modelová komprimácia bola vykonaná na parametroch predtrénovanéj sieti DDPM niekoľkými kvantizačnými a prerezávacími metódami. Tieto metódy boli vyhodnotené na troch rôznych obrázkových dátových sadách. Výsledky potvrdzujú, že implementované kompresné metódy sú vhodne pre nasadenie difúznych modelov na malých zariadeniach s obmedzenými zdrojmi alebo na zníženie ich výpočetnych prevádzkových nákladov. Detailed record
	Deep Neural Network Pruning for Text Recognition Petráš, Simon ; Hradiš, Michal (referee) ; Kišš, Martin (advisor) This document is a work on pruning neural network for handwriting recognition. The aim of the work is to create a program for pruning the network. We prune two types of neural networks, namely convolutional and recurrent neural networks. During the pruning of the convolution part, various criteria of parameter selection were experimented with. The result of the work is a model that achieves 20% acceleration while increasing the network inaccuracy by only 0.4%, but also a number of other models that are faster but also acquire higher inaccuracies. Detailed record

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