muskanmahajan37/practical_dl

DL course co-developed by YSDA, HSE and Skoltech

Deep learning course

This repo supplements Deep Learning course taught at HSE @fall'19. For previous iteration visit the spring19 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).
• Deadlines & grading rules can be found at this page.
• 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, adaptive optimization methods
  • Seminar: Neural networks in numpy
  • Homework 1 is out!
  • Please begin worrying about installing pytorch. You will need it next week!

• week02 Catch-all lecture about deep learning tricks

  • Lecture: Deep learning as a language, dropout, batch/layer normalization, other tricks, deep learning frameworks
  • Seminar: PyTorch basics

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