Distributed Computing
ETH Zurich

Hands-On Deep Learning (HS 2025)

Time: Monday 12:15-16:00
Place: HG G1
Language: English
Register: for this course on myStudies
Contact Person: Susann Arreghini
Student TAs:
Head TA: Till Aczel

This lab offers hands-on deep learning exercises using PyTorch, covering computer vision, audio processing, graph neural networks, natural language processing, reinforcement learning, and generative AI. The material is organized into six topics, each spanning two weeks. The course is conducted on CodeExpert, sign-up. Important: you need to sign up in myStudies before signing up in CodeExpert!

Prerequisites

This course has two prerequsites:

Passing Requirements

To pass the lab, you must earn 150 points out of a possible 180 points. Each topic is worth 30 points, so aim to earn at least 25 points per topic. You can see your progress on CodeExpert. The point distribution per topic is as follows:

Task Points Comments
Session 4 Earn 2 points for attending half a session.
Notebook 6 A solved notebook gets the full 6 points.
Challenge 6 Minimal solution: 1 point; Excellent solution: 6 points.
Discussion 14 All discussions receive the full 14 points.

If you miss a session or discussion for a valid reason (e.g., doctor's note, military service, exam), please email Susann Arreghini. Missed sessions with a valid, documented excuse will earn session points. Missed discussions with a valid excuse can be re-taken durring a session. Not valid reasons: attending other lectures, courses, programs, volunteering, etc. Students are allowed to re-take at most one discussion due to an unexcused absence, which must also be done during a session.

Session

In each session, students work independently or in small groups to solve the notebook and challenge. Most students will need a few extra hours to finish everything after the session. TAs are available to answer questions and provide guidance. Bring your laptop to the session. A limited number of machines will also be available in the room.

Notebook

Each topic is explored using a Python notebook. While collaboration with peers is encouraged, you are required to write and submit your own solutions. The notebook links are listed bellow.

The notebooks require GPUs, and you have two options for accessing them:

Challenge

The challenge builds on the material covered in the notebook and is available on CodeExpert. Remember, do not copy anyone else’s code or solutions. After submitting your solution, you receive feedback on the number of points earned immediately after your model finishes training. The top three submissions for each challenge will receive a small prize!

Discussion

The goal of the discussion is to help you better understand the topic and receive feedback from a TA. Each discussion will take about 15 minutes, and you'll be paired with another student. Please ensure that you sign up in CodeExpert for a discussion slot in advance.

Schedule

Sessions and discussion alternate every Monday. The notebook and challenge must be submitted on the Friday of the session week.

Topic Topic TA Notebook Link Date / Deadline
Session Notebook and Challenge Discussion
Introduction to Deep Learning Till Aczel 22.09.2025 26.09.2025 29.09.2025
Computer Vision and Audio Luca Lanzendörfer 06.10.2025 10.10.2025 13.10.2025
Graph Neural Networks Samuel Dauncey 20.10.2025 24.10.2025 27.10.2025
Natural Language Processing Andreas Plesner 03.11.2025 07.11.2025 10.11.2025
Reinforcement Learning Saku Peltonen 17.11.2025 21.11.2025 24.11.2025
Generative Computer Vision Samuel Dauncey 01.12.2025 05.12.2025 08.12.2025

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