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 2-122
Practices: Tuesdays 14:15, Liivi 2-122

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.

Grading

• 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.

Contacts

Raul Vicente, raulvicente@gmail.com
Tambet Matiisen, tambet.matiisen@gmail.com
Ardi Tampuu, arditampuu@gmail.com