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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
Students will be able to: (i) select the appropriate neural network architecture for the selected data type (ii) design the neural network model and select the appropriate optimization algorithm for training |
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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org |
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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
https://moodle.vscht.cz/enrol/index.php?id=55 |
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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
Feed-forward neural networks
Regularization of neural network models
Convolutional neural networks
Recurrent neural networks
Transformer architecture
Design and optimization of neural networks in various environments - Python, MATLAB |
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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
basic programming skills in Python, MATLAB (at least one of them) are advisable |
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Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
The student passes the practicals by submission of sufficient number of assignments (obtaining the appropriate number of points, including bonus points). The assignments are announced regularly during the whole semester. The student can choose which of the assignments to work on in order to obtain the necessary number of points. The written exam test consists of randomly selected questions from a set of previously announced exam questions. Classification in the exam can be improved or replaced by submission of an extended number of assignments (obtaining the extended number of points).
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Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 1 | 28 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1 | 28 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 140 / 140 |