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.
Lectures: Mondays 16:15 (Delta 1021 or 1008)
Group 2: Tuesdays 12:15 in Delta 2048
Group 1: Wednesday 10:15 in Delta 1022
Due to current situation there is an extension to homework submission times, the deadline is extended by one week.
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.
- 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%!).
Raul Vicente, firstname.lastname@example.org
and teaching assistants:
- Ardi Tampuu, email@example.com
- Anti Ingel, firstname.lastname@example.org
- Oriol Corcoll, email@example.com