pkliui/practical_dl

DL course co-developed by YSDA, HSE and Skoltech

Deep learning course

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.

General info

• 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

Syllabus

• 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!

Contributors & course staff

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