DL course co-developed by HSE, YSDA and Skoltech
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README.md

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 - Binder
  • 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)