This repo supplements Deep Learning course taught at YSDA and 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.
- 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
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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!
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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
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