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.

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.