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.
- 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
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