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 (Liivi 2-111 or Liivi2-405)
Group 1: Tuesdays 10:15 in Liivi 2-206
Group 2: Tuesdays 16:15 in Liivi 2-405
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 60% 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%!).
- There is no exam.
Raul Vicente, firstname.lastname@example.org
and teaching assistants: