This repository contains materials of the Deep Learning course taught at the Faculty of Computer Science of HSE University.
Details about the course organization can be found at the wiki page (in Russian).
- Introduction (slides; homework)
- Core concepts
- Advanced topics
- Applications to Computer Vision (slides; homework)
- Applications to Natural Language Processing (slides; homework)
- Transformer models (slides; homework)
- Adversarial X (slides; homework)
- Probabilistic models (slides, homework)
- Differentiable programming (slides, homework)
- Non-differentiable models (slides, homework)
- Invited talks: TBA
Materials for previous iterations (taught by Anton Osokin) that heavily influenced this course can be found at https://github.com/aosokin/dl_cshse_ami
The content of lectures and assignments is distributed under the Apache 2.0 license: you can use and redistribute it for any purposes, as long as you refer to this course as the origin of the content.