Distributed Computing
ETH Zurich

Hands-On Deep Learning (HS 2024)


When: Monday 12:15-16:00
Place: HG G1
Language: English
Register: for this course on myStudies
Head TA: Till Aczél

This lab introduces deep learning through the PyTorch framework in a series of hands-on exercises, exploring topics in computer vision, natural language processing, audio processing, graph neural networks, and representation learning. You'll explore common and advanced neural architectures, understand network structures, and apply them to solve examples and challenges.


This course has two prerequsites:


This course is organized into specific topics, with each topic spanning two weeks. The detailed schedule can be seen below.


In each session, students work independently or in small groups, following a Python notebook, to explore various aspects of the topic of the given session. There are going to be TAs present at every session, answering students' questions and providing guidance where necessary.

Bring your laptop. Some machines will also be available in the room. The exercises require GPUs. You have two alternatives:


The submission consists of 2 parts:

The submission deadline for each topic is Friday of the session week. Collaboration with colleagues is encouraged, but everyone must write their own programs. Do not copy other people's code or solutions!


To demonstrate your understanding of the topic and receive feedback on your work, you will present to a TA. Each presentation lasts approximately 10 minutes, during which the TA will ask you questions about your notebook and the challenge. Two students will be teamed up in a presentation. You need to sign up to a presentation slot.

Passing Requirements

To pass the lab, you need to earn 18 credits. You can earn a maximum 4 credits for each topic:

If you have a valid reason for missing a session or presentation (e.g., doctor's note, military service), please email Till Aczél. If you miss a presentation with a valid excuse, we will review your submission and award you 2 presentation credits.


Topic Topic TA Session Submission Presentation
Introduction to Deep Learning Till Aczél September 23 September 27 September 30
Computer Vision and Audio Luca Lanzendörfer Oktober 7 Oktober 11 Oktober 14
Graph Neural Networks Joël Mathys Oktober 21 Oktober 25 Oktober 28
Natural Language Processing Frédéric Berdoz November 4 November 8 November 11
Reinforcement Learning Saku Peltonen November 18 November 22 November 25
Generative Computer Vision Benjamin Estermann December 2 December 6 December 9