Neural Networks (LTAT.02.001)
Important: Please note that during the week from 7th Feb - 13th Feb there will be no classes in our course. We will start on the week 14th Feb - 21st Feb. Check the tab Timetable for a detailed schedule.
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: Wednesdays 10:15 (Online over Zoom)
Group 2: Thursdays 14:15 (Online over Zoom)
Zoom link for both groups practice sessions available here. The password is ati.
We will be using this Campuswire forum for communication between students and instructors, questions, etc. If you are registered to the course, you will receive an invitation link to it. In case you don't, for some reason, please contact one of the teaching assistants (contacts are in this page) or use this link to join the forum.
- 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.
However to pass the course you are required to at least get 50% of EACH component (homework, project, and exam).
and teaching assistants: