Since 2016, LauzHack has organized hackathons at EPFL in Lausanne, Switzerland. We also organize tech talks during the school year.
This is a repository for our Deep Learning Bootcamp (Winter 2025 Edition). For previous editions, see Previous Editions section.
- day01 Introduction to Deep Learning and PyTorch
- Lecture: Introduction to bootcamp and Deep Learning
- Seminar: Introduction to
pytorch
- day02 Basic Model Architectures
- Lecture: Fully-connected and Convolutional Neural Networks, ResNet
- Seminar: Models in
pytorchand training pipeline
- day03 Transformer and R&D Coding
- Lecture: Recurrent Neural Networks, BatchNorm, LayerNorm
- Seminar: RNN, LSTM, GRU example
- Lecture 2: Transformer
- Seminar 2: Implementation of Transformer in
pytorch
- day06 Deep Learning for Audio
- Lecture: Representing sound digitally, tasks (denoising, speech recognition, text-to-speech, voice conversion, lip-sync)
- day07 Graph Neural Networks
- Lecture: Graph learning, applications, limitations
- Seminar: PyTorch-based examples of training GCN and SAGE architectures
- day08 Computer Vision
- Lecture: Diffusion models, Vision Transformers, Object Detection, Generalizability, Test-Time Training
- Seminar: Diffusion models and test-time training with MNIST
Bootcamp materials and teaching were delivered by:
- Petr Grinberg
- Seyed Parsa Neshaei
- Eric Bezzam
- Ali Hariri
- Nikita Durasov
- Federico Stella (Previously)
- Atli Kosson (Previously)
- Cristian Cioflan (Previously)
- Skander Moalla (Previously)
- Vinitra Swamy (Previously)