Deep learning course

This repo follows Fall2018 track for HSE students. For previous iteration with complete materials visit the master branch.

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

General info

• Create cloud jupyter session from this repo -
• Telegram chat room (russian).
• YSDA deadlines & admin stuff can be found at the YSDA course wiki (ysda students only).
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
• Grading, lateness penalties and other formalities - see this page

Syllabus

• week01 (10.09.2018) Basics of neural networks

  • Lecture: ML recap. Neural nets 101: backprop, intizlization, adaptive SGD
  • Seminar: Neural networks in numpy (deadline in 10 days)

• week02 (17.09.2018) Deep learning stuff

  • Lecture: An umbrella-lecture for deep learning frameworks, some philosophy, tips & tricks
  • Seminar: Automatic gradients (pytorch | tensorflow | theano)

Contributors & course staff

Course materials and teaching performed by (in random order)

• Victor Lempitsky - YSDA main track lectures (1-11)
• Victor Yurchenko - intro notebooks, YSDA admin stuff
• Victoria Chekalina - seminars, HSE hw checkup, admin stuff
• Vadim Lebedev - notebooks, admin stuff
• Dmitry Ulyanov - notebooks on generative models & autoencoders
• Fedor Ratnikov - pytorch & nlp notebooks, some lectures
• Oleg Vasilev - notebooks, technical issue resolution
• Arseniy Ashukha - image captioning materials
• Mikhail Khalman - variational autoencoder materials