Deep Learning 2017

Course Materials

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.

• Other Materials
• Lecture Slides
• Old Exams (with Answers)

Instructors

• Kazi Ripon
• Keith Downing

Course Meetings

• Where: Room H1 ("Hovedbygget")
• When: Tuesdays kl 16:15 - 18:00 (throughout the semester)

FIRST MEETING: September 12th, 2017

Final Exam

Date: Friday, December 1, 2017

On the exam date, all students will meet up and be given a task that will take no more than one hour to complete. That will be the basis of the final grade.

• The 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.