![]() | Hello, On Monday, April 28 after 5 pm, some of the university's information systems will be unavailable due to maintenance work. Affected systems are the Education Information System, Financial IS (iFIS), MIS/OBD/Verso, Kopla and related agendas. It is anticipated that everything should be back online and running by Tuesday morning. Work with e-mail or shared drives will not be affected. Computer center |
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The course is focused on comprehension of commonly used neural network architectures, suitable for various types of solved problems and processed data. Lectures cover the necessary theory, but are mainly focused on practical aspects of neural network design. For seminars, students will try to train the designed models of neural networks and further optimize them.
Last update: Cejnar Pavel (14.06.2022)
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A: Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org Last update: Cejnar Pavel (22.09.2023)
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1. Introduction to neural networks. 2. Feed-forward neural networks, basic architectures and activation functions. 3. Optimization algorithms for training. 4. Regularization of neural network models. 5. Frameworks for neural network development. 6. Convolutional neural networks, normalization. 7. Architectures suitable for deep convolutional neural networks. 8. Architectures for object detection and segmentation. 9. Pre-training and fine-tuning of deep neural networks. 10. Recurrent neural networks and problems of their training. 11. Recurrent neural networks - bidirectional and deep recurrent networks. 12. Transformer architecture. 13. Design and optimization of neural networks in various environments. 14. Reinforcement learning. Last update: Cejnar Pavel (22.09.2023)
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