===== Lab/Seminar information =====

Two types of labs (tutorials) will be proposed for the course (alternating):
  * practical labs in which students will implement selected methods discussed in the course and experiment with them,
  * theoretical labs in which students will discuss solutions of theoretical assignments (made available before the class).

The solutions of the practical labs have to be submitted using the [[http://cw.felk.cvut.cz/upload/|upload system]]
  * Your task will be to program a solution of the assigned problems. You have to hand out your code and a report. The report has to contain only answers to the assignments (nothing else). 
  * The programming language is Python/PyTorch.
  * The deadline for submitting your solutions will be 4 weeks after the date of assignment. This is a hard deadline.
  * Not submitting a lab is equivalent to getting 0 points. You need at least of 50% of total lab points to pass.

==== Submission Regulations ====

You may choose from the following submission variants:

  - Python code in source form, report in pdf
  - Python code in source, Ipython notebook source printed as pdf. The report in this case can be part of the notebook

Sharing the code that is not a required part of the assignment is permitted, for example code helping with visualization.


==== Schedule ====

See [[lectures|syllabus]]

/**
^Week ^Date ^Topic ^Lecturer ^Materials ^Deadline ^
|1.| 20.2 | (no lab) | - | | |
|2.| 27.2 |Lab: generative vs. discriminative learning I | BF | {{:courses:bev033dle:gen-bounds-1.pdf| }} {{:courses:bev033dle:model.tgz| model}}| 26.3.|
|3.| 5.3 |Seminar | BF | | |
|4.| 12.3 |Lab: generative vs. discriminative learning II | BF | | |
|5.| 19.3 |Seminar | AS / BF | | |
|6.| 26.3 |Seminar | AS | | |
|7.| 2.4 |Lab: PyTorch | AS | | |
|-| 9.4 | (friday schedule) | - | | |
|8.| 16.4 |Lab: Deep learning pipeline | AS | | |
|9.| 23.4 |Lab: Weight initialisation, batch normalisation, adaptive SGD | AS | | |
|10.| 30.4 |Seminar | AS | | |
|11.| 7.5 |Lab: Network visualisation, adversarial patterns | BF | | |
|12.| 14.5 |Lab: Generative networks| BF | | |
|13.| 21.5 |Seminar | BF | | |

**/