Table of Contents

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Labs and Seminars

Two types of labs (tutorials) will be proposed for the course (alternating):

Teachers:

Schedule

(contents will be updated)

Date Topic Teacher Reading
22.02.2024 Lab 1: Preparations, Double Descent AS NNTD lecture 1 (except 2.3, 5)
29.02.2024 Seminar 1 (lecture 1) BF
07.03.2024 Lab 2: Backpropagation, Computational graph AS
14.03.2024 Seminar 2 (lectures 2,3) JS
21.03.2024 Lab 3: CNN Fine-tuning NE
28.03.2024 Seminar 3 (lectures 4,5) JS
04.04.2024 Lab 4: From Scratch: Initialization & regularization JS
11.04.2024 Seminar 4 (lectures 6,7) AS
18.04.2024 Lab 5: CNN visualization & adversarial patterns BF
25.04.2024 Seminar 5 (lectures 8,9) AS
02.05.2024 Lab 6: Metric learning JS
09.05.2023 — no class —
16.05.2024 Lab 7: VAE BF
23.05.2024 Seminar 6 (lectures 10,11) AS

Seminars

The seminar assignments are published 1 week in advance before the seminar. You are expected to prepare for it at home. We discuss the problems and solutions in the class. You are not required to submit you solutions, but if you solved a problem you will be invited to present it in the class. Seminars are not scored by points but they are important for gaining technical understanding, which will be finally evaluated in the written exam.

Examples of problems with solutions: examples.pdf (to be updated)

Labs

The solutions of the practical labs have to be submitted using the upload system

Submission Regulations

You may choose from the following submission variants:

  1. Report in pdf and Python source code
  2. Annotated Jupyter notebook with inline results and Python source code if applicable

Please do not submit data and any other redundant files. Sharing code that is not a required part of the assignment is permitted, for example additional visualization code or test cases for debugging.