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Financial market analysis using deep learning algorithm
Nimrichter, Adam ; Burget, Radim (referee) ; Mašek, Jan (advisor)
The thesis deals with methods for analysis of financial markets, focused on cryptocurrencies. The theoretical part, in a context of virtual currencies, describes block-chain technology, financial indicators and neural networks with recurrent architectures. Main goal is to create a system for giving a recommendation either for buy, or sell the currency. The system consists of designed financial strategy and predicted value of the currency, for which is used financial indicators and LSTM neural network. Tests were performed on Bitcoin, Litecoin and Ethereum historical data from year 2017.
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Detecting the Occurrence of Objects in a Video
Šamánek, Jan ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This bachelor thesis deals with detection of objects in videos by using primarily convolution neural networks and creating simple user interface, which allows user to choose classification model and use it to analyze video or train given model on own dataset. First part is dedicated to description of machine learning and neural networks. After that follows the section about image description and image classification using machine learning algorithms and data augmentation. Last part deals with describtion of own design of neural network and user interface and describing achieved results.
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Detecting the Occurrence of Objects in a Video
Šamánek, Jan ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This bachelor thesis deals with detection of objects in videos by using primarily convolution neural networks and creating simple user interface, which allows user to choose classification model and use it to analyze video or train given model on own dataset. First part is dedicated to description of machine learning and neural networks. After that follows the section about image description and image classification using machine learning algorithms and data augmentation. Last part deals with describtion of own design of neural network and user interface and describing achieved results.
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Financial market analysis using deep learning algorithm
Nimrichter, Adam ; Burget, Radim (referee) ; Mašek, Jan (advisor)
The thesis deals with methods for analysis of financial markets, focused on cryptocurrencies. The theoretical part, in a context of virtual currencies, describes block-chain technology, financial indicators and neural networks with recurrent architectures. Main goal is to create a system for giving a recommendation either for buy, or sell the currency. The system consists of designed financial strategy and predicted value of the currency, for which is used financial indicators and LSTM neural network. Tests were performed on Bitcoin, Litecoin and Ethereum historical data from year 2017.
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