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Searching relevant articles in extensive collections
Vojt, Ján ; Novák, Jiří (advisor) ; Bartoš, Tomáš (referee)
Searching text in articles is usually implemented with fulltext search. Using more advanced techniques however, it is possible to achieve significantly better results. The subject of this work is to create a universal library for searching extensible collections, specialized in czech language. The library makes use of tools capable of working with morphology while considering importance of words. It also conducts an experiment with word pairs, which adds context into the search process. The success rate of this experiment is tried on an extensible collection of data. Created library is a unique tool for processing extensible collections of czech text, while at the same time it is ready for further extension by new languages and methods.
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Deep neural networks and their implementation
Vojt, Ján ; Mrázová, Iveta (advisor) ; Božovský, Petr (referee)
Deep neural networks represent an effective and universal model capable of solving a wide variety of tasks. This thesis is focused on three different types of deep neural networks - the multilayer perceptron, the convolutional neural network, and the deep belief network. All of the discussed network models are implemented on parallel hardware, and thoroughly tested for various choices of the network architecture and its parameters. The implemented system is accompanied by a detailed documentation of the architectural decisions and proposed optimizations. The efficiency of the implemented framework is confirmed by the results of the performed tests. A significant part of this thesis represents also additional testing of other existing frameworks which support deep neural networks. This comparison indicates superior performance to the tested rival frameworks of multilayer perceptrons and convolutional neural networks. The deep belief network implementation performs slightly better for RBM layers with up to 1000 hidden neurons, but has a noticeably inferior performance for more robust RBM layers when compared to the tested rival framework. Powered by TCPDF (www.tcpdf.org)
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Deep neural networks and their implementation
Vojt, Ján ; Mrázová, Iveta (advisor) ; Božovský, Petr (referee)
Deep neural networks represent an effective and universal model capable of solving a wide variety of tasks. This thesis is focused on three different types of deep neural networks - the multilayer perceptron, the convolutional neural network, and the deep belief network. All of the discussed network models are implemented on parallel hardware, and thoroughly tested for various choices of the network architecture and its parameters. The implemented system is accompanied by a detailed documentation of the architectural decisions and proposed optimizations. The efficiency of the implemented framework is confirmed by the results of the performed tests. A significant part of this thesis represents also additional testing of other existing frameworks which support deep neural networks. This comparison indicates superior performance to the tested rival frameworks of multilayer perceptrons and convolutional neural networks. The deep belief network implementation performs slightly better for RBM layers with up to 1000 hidden neurons, but has a noticeably inferior performance for more robust RBM layers when compared to the tested rival framework. Powered by TCPDF (www.tcpdf.org)
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Searching relevant articles in extensive collections
Vojt, Ján ; Novák, Jiří (advisor) ; Bartoš, Tomáš (referee)
Searching text in articles is usually implemented with fulltext search. Using more advanced techniques however, it is possible to achieve significantly better results. The subject of this work is to create a universal library for searching extensible collections, specialized in czech language. The library makes use of tools capable of working with morphology while considering importance of words. It also conducts an experiment with word pairs, which adds context into the search process. The success rate of this experiment is tried on an extensible collection of data. Created library is a unique tool for processing extensible collections of czech text, while at the same time it is ready for further extension by new languages and methods.
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