Visual recognition: neural networks 1000-2M18RO
1. Introduction to Visual Recognition.
2. Image Classification, Loss Functions and Optimization.
3. Introduction to Neural Networks.
4. Convolutional Neural Networks.
5. Training Neural Networks. Deep Learning Hardware and Software.
6. Convolutional Neural Networks: Architectures.
7. Recurrent Neural Networks.
8. Object Detection, Action Recognition, Semantic and Instance Segmentation, Video Understanding.
9. Generative Models.
10. Visualizing and Understanding.
11. Reinforcement Learning.
Type of course
Requirements
Assessment criteria
Laboratories: projects.
Lecture: written exam.
Bibliography
* R. Szeliski, Computer Vision: Algorithms and Applications, Springer Science & Business Media, 2010.
* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: