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Introduction to Deep Learning

Welcome to the web page of the class Introduction to Deep Learning at the Technical University of Košice, a course that is taught in the summer semester in the first year of MSc. studies for students of Intelligent Systems. The course is a continuation of the course Neural networks. This web page provides all necessary information and materials for the course.

Join us in our facebook group.

Grading

To successfully pass this course, you have to meet the following requirements:

  1. attendance at lectures and labs (3 absences at most)
  2. hand in the semestral project (see lower)
  3. get at least 21 points during the semester
  4. pass the exam (get at least 31 points)

Course plan

Lectures Labs Team project
Week 1
17. 2. - 23. 2.		 Indtroduction to Neural Nets with Tensorflow and Keras
codes		 web deployment tutorial
(Andrij David, MSc.)		 creating teams, choosing assignments
Week 2
24. 2. - 1. 3.		 TBA basics of Python, Tensorflow and Keras
(Ján Magyar, MSc.)
Week 3
2. 3. - 8. 3.		 TBA convolutional NN for detection
(Fouzia Adjailia, MSc.)		 research report
Week 4
9. 3. - 15. 3.		 TBA Evaluation of NNs
(Miroslav Jaščur, MSc.)
Week 5
16. 3. - 22. 3.		 TBA CNN for segmentation and its evaluation
(Patrik Sabol, MSc.)		 system design and architecture
Week 6
23. 3. - 29. 3.		 TBA RNN for time series and tabural data
(Andrij David, MSc.)
Week 7
30. 3. - 5. 4.		 TBA RNN for text processing
(Andrij David, MSc.)
Week 8
6. 4. - 12. 4.		 TBA presentation of first versions proof of concept
Week 9
13. 4. - 19. 4.		 TBA Easter
Week 10
20. 4. - 26. 4.		 TBA generative adversarial networks
(Ján Magyar, MSc.)		 progress report
Week 11
27. 4. - 3. 5.		 TBA deep reinforcement learning
(Lukáš Hruška, MSc.)
Week 12
4. 5. - 10. 5.		 TBA handing in assignments final version
Week 13
11. 5. - 17. 5.		 TBA — —

Assignment

During the semester, each student must participate in the completion of an assignment. Assignments are done by teams of three or four students. Each team project must contain the following:

  • a specified research goal
  • an overview of state-of-the-art solutions
  • front-end application deployed on a server
  • a trained DL model
  • documentation
  • a research paper presenting the results of the team project (the paper can be published in academic journals).

Topics:

  1. Land cover classification (consultant Patrik Sabol, MSc.)
  2. assignment in cooperation with US Steel (consultant Norbert Ferenčík, MSc.)
  3. processing the Guatemala dataset (consultant Miroslav Jaščur, MSc.)
  4. Neural Code Completion (consultant Andrij David, MSc.)
  5. Super-resolution with GANs (consultant Ján Magyar, MSc.)
  6. deep reinforcement learning (consultant Lukáš Hruška, MSc.)
  7. topic to be announced (consultant Fouzia Adjailia, MSc.)
  8. explainable AI (consultant Ivan Čík, MSc.)

** Sign for assignments **

Sources