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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: 2026-06-01 11:33:22

Abstract

This lab offers hands-on deep learning exercises using PyTorch, covering computer vision, audio processing, graph neural networks, natural language processing, reinforcement learning, and representation learning. The material is organized into six topics, each spanning two weeks.

Objective

The goal of this course is to introduce students to both fundamental and advanced neural network architectures, helping them understand how these models work through practical examples. Students will study different types of networks as building blocks and apply them to solve problems and complete course challenges. By the end of the course, students will be familiar with architectures such as multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformers, graph-based networks (e.g., graph convolutional and attention networks), and autoencoders. They will also learn how to train a network from scratch or fine-tune a pre-trained model for various tasks and data types.

Resources

Lecture Notes

Python Notebooks will be distributed to students before every session.

Learning Materials (Links)

General Information

Language
English
Levels
BSC
Frequency
Semesterly recurring

Examination

Type
ungraded semester performance

Registration & Places

Max Places
200

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