This repo supplements Deep Learning course taught at YSDA and HSE @spring'21. For previous iteration visit the fall20 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 LMS (ysda students only).
- Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
- week01 Intro to deep learning
- Lecture: Deep learning -- introduction, backpropagation algorithm
- Seminar: Neural networks in numpy
- Homework 1 is out!
- Please begin worrying about installing pytorch. You will need it next week!
Course materials and teaching performed by
- Victor Lempitsky - all main track lectures (1-11)
- Victor Yurchenko - intro notebooks, admin stuff
- Vadim Lebedev - notebooks, admin stuff
- Dmitry Ulyanov - notebooks on generative models & autoencoders
- Fedor Ratnikov - pytorch & nlp notebooks, one bonus lecture
- Oleg Vasilev - notebooks, technical issue resolution
- Arseniy Ashukha - image captioning materials
- Mikhail Khalman - variational autoencoder materials