TDT-76: Deep Learning (a theory module)
This is the instructor's page for TDT-76.
One book: "Deep Learning (2016)", Goodfellow, Bengio and Courville, MIT Press.
- Chapters 1-5. (General Background)
- Chapters 6, 7, 8, 9, 10, 11, 12, 14, 15. (Deep Learning in Detail)
The point is not to understand every detail of every chapter, but rather, to absorb the
essence of each. This does not mean that you should just skip all the mathematics, but
try not to get too bogged down in it. If you don't understand a complex mathematical
or algorithmic section, skim it and try to come back to it later if it turns out to
be essential for understanding many other parts of the book.
- Helge Langseth (firstname.lastname@example.org)
- Keith Downing (email@example.com)
Eliezer de Souza da Silva (firstname.lastname@example.org)
Where: Room 424 ("Vembi") in building "HÃ¸gskoleringen 3"
When: Fridays kl 14:15 - 16:00 (throughout the semester)
FIRST MEETING: September 7th, 2018
- Exam Dates: Wednesday - Friday, November 28-30, 2018
- Due to the overwhelming popularity of this module, we are forced to spread the
evaluation out over 3 days. Each student will receive information from IDI as to
the exact day of their exam.
- On your exam date, you will meet up to both a) demonstrate your project,
and b) take a short oral exam (on the theoretical aspects of the course). Results of
both activities will be combined to form the final grade.
- The oral exam will not focus heavily on the most complex linear algebra and calculus
associated with neural
networks, but a basic understanding of the
primary mathematical and computational concepts (e.g., tensors, partial derivatives,
objective functions, etc.) and their
use in Machine Learning (in general) and Deep Learning (in particular) will be necessary to
properly prepare for the exam.
|Redaktør: Kontorsjef: Eivind Voldhagen
||Kontaktadresse: Audun Liberg
||Sist oppdatert: 01.01.1970