Deep Learning Bootcamp (Winter 2025)

​This is the Winter 2025 edition of our Deep Learning Bootcamp! Join us for these two weeks and master your deep learning skills! If you want some specific materials, write to us on discord or by email.

Hybrid format: lessons on campus with stream to Zoom and recording. You can be fully remote, fully on campus or you can do some days online and some offline, as you wish.

Participation is free. Registration for the event on Luma is required. Language: English

Join our dl-bootcamp-winter-2025 Discord server channel for announcements and discussions. Time schedule, Zoom link, etc. are there.

Event description:

DL Bootcamp has two stages:

1. ​​Introduction to DL – for students with little or no DL background. (February 3rd - 7th)

2. ​​Advanced specific topics – for those who already know DL or participated in part 1. (February 10th -14th)

​​The core features of our bootcamp:

1. ​​Lectures given by PhD experts in the field

2. ​Theoretical and practical materials. Both presented in intuitive and easy-to-follow manner, giving you all the necessary information to apply deep learning in your own projects

3. ​Beginner-friendly

4. ​​Discord server with all participants and mentors

5. ​​No matter if you want to do research work or industry work in the future, you can learn all the necessary skills here

6. ​​Hybrid Format: lessons on campus and Zoom. All lessons will be recorded

7. ​​Free, no grade, no credits — no stress self-development

8. ​​LauzHack participation certificate will be given to all participants

​​Topics:
Introduction: PyTorch, Deep Learning concept, Datasets, Optimizers, Schedulers, Loss Functions, FC, CNN, RNN, Transformer, Experiment tracking (WandB)

Deep Learning in Audio: Signal Processing, Overview of tasks, Speech To Text (Automatic Speech Recognition), Keyword Spotting, Source Separation, Lip-Syncing

NLP: Introduction to NLP, Past vs Modern approaches, modeling texts with RNNs and Transformer, seq2seq machine translation, text classification (BERT), text generation (GPT), and text generation at scale (LLMs)

CV: TBA

Guests' Material: Graph Neural Networks (GNNs)

Prerequisites:
Python knowledge: Classes, functions, for-loops, etc.
NumPy would be helpful. Have a look at this notebook and this lab from COM-202 course to get familiar with the basics. You can also check some of the PyTorch basics here, however, we will go through them.

​​Lessons format:
Lectures: theory, task description, model architectures, core algorithms for the specific topic, etc.
Seminars: practice, jupyter notebook which will be filled during the class with the mentor, shows how theory looks in practice

Time Schedule:
From 16:00 to 19:00. Monday-Friday. The schedule for each week: will be available in the pinned message on Discord

Check out our summer edition here.

If you have any questions send an email to lauzhack@epfl.ch or contact us in Discord server.