Hands-On Deep Learning (FS 2023)
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.
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: TBA
Lab language: English
Lab registration: Link
Requirements: Bring your own laptop
- 18.12.2022: Website goes up.
|Lab||Date||Material||Colab TA||Session TAs|
|Introduction to Deep Learning||TBD||Colab Notebook(link TBA)||Peter Belcák||Peter Belcák|
|Perceptive Computer Vision||TBD||Colab Notebook (link TBA)||Peter Belcák||Peter Belcák|
|Generative Computer Vision||TBD||Colab Notebook (link TBA)||Benjamin Estermann||Benjamin Estermann|
|Natural Language Processing||TBD||Colab Notebook (link TBA)||Florian Grötschla||Florian Grötschla|
|Audio Processing||TBD||Colab Notebook (link TBA)||Peter Belcák||Peter Belcák|
|Graph Neural Networks||TBD||Colab Notebook(link TBA)||Joël Mathys||Joël Mathys|