Neural Networks (LTAT.02.001)
The course presents the main concepts of the theory and practice of modern neural networks. It also gives students the basic understanding and tools to be able to independently apply neural networks to real problems.
The lectures are based on the book "Deep Learning" by Ian Goodfellow and Yoshua Bengio and Aaron Courville. In practices we are following the excellent Stanford university course "Convolutional Neural Networks for Visual Recognition" by Andrej Karpathy, Justin Johnson and Fei-Fei Li.
Tuesdays 14:15 (Online over Zoom)
Zoom link for the lectures available here. The password is ati.
Group 1: Thursdays 14:15 (Online over Zoom)
Group 2: Thursdays 16:15 (Online over Zoom)
Zoom link for both groups practice sessions available here. The password is ati.
We will be using this piazza forum for communication between students and instructors, questions, etc. If you are registered to the course, you should have received an invitation link to it. In case you haven't, for some reason, please contact one of the teaching assistants (contacts are in this page).
- Homeworks will give 30% of the final grade.
- Practice exam will give 30% of the final grade.
- A project will give 40% of the final grade.
- Bonus homework gives additional 10% of the final grade (yes, you can get 110%!).
However to pass the course you are required to at least get 50% of EACH component (homework, project, and exam).
and teaching assistants: