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

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).

A: Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org

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.

https://moodle.vscht.cz/enrol/index.php?id=55

basic programming skills in Python, MATLAB (at least one of them) are advisable