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227-0085-59L 2 Credits BSC D-ITET , D-INFK

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Hands-On Deep Learning

Lecturers & Examiners: Prof. Dr. Roger Wattenhofer

VVZ CR 4.65

Last Updated: 2026-02-05 16:38:16

Abstract

The lab introduces deep learning through the PyTorch framework in a series of hands-on exercises. You will learn about various network structures as building blocks and how to use them to solve introductory examples and course challenges. After attending the course, you will be familiar with multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformer encoders,

Objective

The objective of this course is to expose students to both common and cutting-edge neural architectures and to build intuition about their inner working by the means of examples. Students learn about various network structures as building blocks and use them to solve worked examples and course challenges. After attending this course, students will be familiar with multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformer encoders, graph convolutional/isomorphism/attention networks, and autoencoders. After attending this course, students will be familiar with multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformer encoders, graph convolutional/isomorphism/attention networks, and autoencoders.

Resources

Lecture Notes

Python Notebooks will be distributed to students before every session.

Learning Materials (Links)

Main link
Website

General Information

Language: English
Levels: BSC
Frequency: Semesterly recurring

Examination

Type: ungraded semester performance

Course Components

Type	Title	Time & Place	Hours
practical/laboratory course	Hands-On Deep Learning	Thu 13:15-17:00 (HG G 1)	32 h semesterly

Offered In

Computer Science Bachelor
- Electives (Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.)
Electrical Engineering and Information Technology Bachelor
- Laboratory Courses, Projects, Seminars (A minimum of 15 cp must be achieved in the category "Laboratory Courses, Projects, Seminars".)
  - Additional Subjects