Distributed Computing
ETH Zurich

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

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Schedule

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