Hands-On Deep Learning (HS 2022)
This lab introduces deep learning through the PyTorch framework in a series of hands-on exercises, exploring topics in computer vision, natural language processing, graph neural networks, and representation learning.
In this lab, you will learn common as well as cutting-edge neural architectures. You will learn about various network structures as building blocks, and how to use them to solve introductory examples and course challenges. After attending this course, you will be familiar with multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformer encoders, graph convolutional/isomorphism/attention networks, and autoencoders.
Lab prerequisites: Students must have some familiarity with the ideas behind deep learning. Any course that teaches the fundamentals of deep learning (e.g. Computational Thinking) is sufficient, and so is an adequate amount of self-study.
Lab structure: 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. The step-by-step exercises are designed with intuition-building in mind. There are going to be TAs present at every session, answering students' questions and providing guidance where necessary. The final two sessions are going to consist of machine-learning challenges building on the previously learned material.
Lab time: Tuesday 8:00-12:00 (see dates below), ETZ K 91. (The course starts late in the semester to better align it with the Computational Thinking lecture.)
Lab language: English
Lab registration: Link
Requirements: Bring your own laptop
- 17.08.2022: Website goes up.
- 05.12.2022: Colab #4 added.
- 18.12.2022: Course website finalised.
|Lab||Date||Material||Colab TA||Session TAs|
|Introduction to Deep Learning||15.11.2022||Colab Notebook # 1 (link retired)||Peter Belcák||Peter Belcák, Joël Mathys|
|Natural Language Processing||22.11.2022||Colab Notebook # 2 (link retired)||Florian Grötschla||Florian Grötschla, Joël Mathys|
|Graph Neural Networks||29.11.2022||Colab Notebook # 3 (link retired)||Joël Mathys||Joël Mathys, Florian Grötschla|
|Computer Vision||06.12.2022||Colab Notebook # 4 (link retired)||Peter Belcák||Peter Belcák, Joël Mathys|
|Challenge||13.12.2022||Colab Notebook # 5 (link retired)||Joël Mathys||Joël Mathys, Peter Belcák|