Deep Learning Bootcamp

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 (Summer 2025 Edition). For previous editions, see Previous Editions section.

Syllabus

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 pytorch and training pipeline
day03 Recurrent Neural Networks
- Lecture: Recurrent Neural Networks, LSTM, GRU
- Seminar: RNN, LSTM, GRU example
day04 Transformer and Normalization layers. Introduction to NLP.
- Lecture: Transformer. BatchNorm, LayerNorm.
- Seminar: Implementation of Transformer in pytorch
day05 Creating convenient Deep Learning pipelines and clean reproducible code.
- Lecture: Logging, Configuration, Reproducibility, and Project-based code development.
day06 Large Language Models (LLMs) and Brain-inspired LLMs.
- Lecture: Introduction to LLMs and how can we improve them through brain-inspiration.
day07 Multimodal deep learning and deep learning for audio.
- Lecture: Introduction to audio domain. Multimodality and Generative AI. Deepfakes.
- Seminar: Basics of audio processing. Keyword-spotting task implementation.
day08 Source Separation and Deepfake Detection.
- Lecture 1: Diffusion models, Source Separation
- Lecture 2: Deepfake Detection, Self-Supervised Models, Graph Neural Networks
- Seminar 2: Audio anti-spoofing, Graph Neural Networks implementation.

For self-practice, we also propose several Projects.

Bootcamp materials and teaching were delivered by: