practical_deeplearning

Attention! This is a new iteration of on-campus deeplearning course. For full course materials '2016, go to this branch

Lecture and seminar materials for each week are in ./week* folders

Coordinates

• HSE classes take place each wednesday, from 18-10 till 21-00, room 501
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
• More stuff TBA

Announcements

• 6.09 - course started, week0 assignment published (see "syllabus")

Syllabus

• week0 ML recap & basic deep learning
  • Lecture: Linear models, stochastic optimization, regularization
  • Seminar: Linear classification, sgd, modifications
    • HW due: 17.09.16, 23.59.
  • Please get these libraries installed for the next seminar.
    • Issue
    • Linux Guidelines
    • TODO guide for TF/pytorch

Stuff

• One rule to rule them all
• Project rules
• Project examples
• Grading system
• Grading table
• Reducing lateness penalty
• Feedback form (anonymous)
• 

Contributors & course staff

Course materials and teaching performed by

• Fedor Ratnikov - lectures, seminars, hw checkups
• Oleg Vasilev - seminars, hw checkups, technical issue resolution
• Arseniy Ashukha - image captioning, sound processing, week7&9 lectures
• Dmitry Ulyanov - generative models, week8 lecture, week12 homework assignment
• Mikhail Khalman - variational autoencoders, lecture 12
• Vadim Lebedev - week0 & week6 homeworks