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The GPU Based Acceleration of Neural Networks
Šimíček, Ondřej ; Jaroš, Jiří (referee) ; Petrlík, Jiří (advisor)
The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.
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Scene Analysis Based on the 2D Images
Hejtmánek, Martin ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This thesis deals with an object surface analysis in a simple scene represented by two-dimensional raster image. It summarizes the most common methods used within this branch of information technology and explains both their advantages and drawbacks. It introduces the design of an surface profile analysis algorithm based on the lighting analysis using knowledge and experiences from previous work. It contains a detailed description of the implemented algorithm and discusses the experimental results. It also brings up options for the possible enhancement of the projected algorithm.
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Building deep networks using autoencoders
Lohniský, Michal ; Veselý, Karel (referee) ; Hradiš, Michal (advisor)
This thesis deals with pretraining deep networks by autoencoders. Components of neural networks are described in first chapters. Rest of chapters aims to deep network trainings and to results of experiments where autoencoder pretraining and Backpropagation algorithm are compared. Results showed positive contribution of autoencoder pretraining, mainly in combination with Finetuning.
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Utilization of deep learning for channel estimation in OFDM systems
Hubík, Daniel ; Staněk, Miroslav (referee) ; Miloš, Jiří (advisor)
This paper describes a wireless communication model based on IEEE 802.11n. Typical methods for channel equalisation and estimation are described, such as the least squares method and the minimum mean square error method. Equalization based on deep learning was used as well. Coded and uncoded bit error rate was used as a performance identifier. Experiments with topology of the neural network has been performed. Programming languages such as MATLAB and Python were used in this work.
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Adaptive controllers with principles of artificial intelligence and its comparison with classical identifications methods
Vaňková, Tereza ; Dokoupil, Jakub (referee) ; Pivoňka, Petr (advisor)
Master’s thesis is focused on the adaptive controllers. The first theoretic part mainly describes the parametric identification, which belongs to the most important part of the adaptive controller’s structure. Classical identification methods (the recursive least squares methods) are firstly mentioned and afterwards the identification methods based on the neural network (the Marquardt-Levenberg algorithm and the new identification algorithm NIA inspired by the neural networks) are described. At the conclusion of the theoretic part there are mentioned the algorithm of the adaptive controller’s tuning which uses the identification parameters (the modified Z-N method) and the tested types of adaptive controllers. Particular results, which were found out by verifying of the adaptive controllers on the simulation and real models, are contained in second, the practical, part of the thesis. Finally, achieved results are compared with the classical discrete PID controller and with the adaptive controller of the B&R company.
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Machine Comprehension Using Commonsense Knowledge
Daniš, Tomáš ; Landini, Federico Nicolás (referee) ; Fajčík, Martin (advisor)
V tejto práci je skumaná schopnosť používať zdravý rozum v moderných systémoch založených na neurónových sieťach. Zdravým rozumom je myslená schopnosť extrahovať z textu fakty, ktoré nie sú priamo spomenuté, ale implikuje ich situácia v texte. Cieľom práce je poskytnúť náhľad na súčasný stav výskumu v tejto oblasti a nájsť sľubné výskumné smery do budúcnosti. V práci je implementovaný jeden z najmodernejších modelov na odpovedanie na otázky a je ďalej použitý na experimenty v rôznych situáciách. Narozdiel od starších prístupov, tento model dosahuje porovnateľné výsledky s najlepšími známymi modelmi aj keď jeho architektúra neobsahuje žiadne prvky zamerané konkrétne na zlepšenie schopnosti zdravo uvažovať. Taktiež boli nájdené štatistické artefakty v populárnej sade dát s otázkami vyžadujúcimi zdravé uvažovanie. Tieto artefakty môžu byť použité štatistickými modelmi na nájdenie správnej odpovede aj v prípadoch, kedy by to nemalo byť možné. Na základe týchto zistení sú v práci poskytnuté odporúčania a návrhy pre výskum do budúcnosti.
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The Use of Means of Artificial Intelligence for the Decision Making Support on Stock Market
Vaško, Jan ; Kříž, Jiří (referee) ; Dostál, Petr (advisor)
Diploma thesis deals with analyzing the possibility of using artificial intelligence, specifically artificial neural networks and fuzzy logic, on the capital markets as a tool to support decision making in business. The Matlab software is used for this purpose. The work is divided into three parts. The first part deals with theoretical knowledge, brief description of the current situationin is covered in a second part and the theoretical solutions are applied to the system in the third section.
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Algorithmic Trading Using Twitter Data
Kříž, Jakub ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis describes creation of prediction system. This system predicts future market development based on stock exchange data and twitter messages analysis. Tweets from two different sources are analysed by mood dictionaries or via recurrent neural networks. This analysis results and technical analysis of stock exchange data results are used in multilayer neural network for prediction. A business strategy is created and tested based on results of this prediction. Design and implementation of prediction system is described in this thesis. This system achieved revenue increase more than 25 % of some business strategies by tweets analysis. However this improvement applies for certain data and timeframe.
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Data analysis from the manufacturing process
Krčmář, Martin ; Honzík, Petr (referee) ; Zezulka, František (advisor)
This thesis deals with the classification of production data using algorithms: neural networks, decision trees and naive bayesian classifier. The neural network is dedicated forward multilayer networks with a learning algorithm of backpropagation. In thesis, these algorithms are described and evaluated their pros and cons. Another part deals with the development of the program in C# for creating these algorithms. The last part is devoted to the evaluation of the results. Bachelor thesis contains a sample of generated clasification models decision tree and bayesian classifier.
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