We’ll train any larger networks over night following the class, and then compare solutions.
All homework are due at pm, but you’ll have one additional hour to upload your assignment to canvas (in case your internet connection is really slow). Collaboration is not allowed for homework or Quizzes.
Prerequisites: Python, basic ML background Textbooks: None We will use Piazza for questions and canvas homework.
CS 342 follows the flipped classroom model, and delivers all course material online.
Please do this as soon as possible, so that you can have the benefit of the accommodations throughout the duration of the course.
We expect that you will treat online discussions as though you are having a civil, respectful discussion with your fellow classmates in the same classroom.
In short, please just respect the right of your colleagues to ask questions and discuss their opinions about the subject matter of our course on the discussion board.
Violators of these discussion rules will simply be shut out from all class communications—email, Piazza, and office hours.
meets MWF 10am-11am UTC 3.102 instructor Philipp Krähenbühl ( philkr (at) ) office hours M 11am-noon GDC 4.824 Please only visit during office hours or with appointment (or if I didn’t reply your email after the third try) TA Xingyi Zhou ( zhouxy (at) cs.) office hours W PM-PM GDC 1.302 (Basement) Desk 5 Brady Zhou ( bzhou (at) cs.) office hours W AM-PM GDC 1.302 (Basement) Desk 1 Ishan Nigam ( ishann (at) cs.) office hours M PM-PM GDC 1.302 (Basement) Desk 2 This course covers the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks.
We will cover both the theory of deep learning, as well as hands-on implementation sessions in pytorch.