Deep learning course

This repo supplements Deep Learning course taught @fall'23. For previous iteration visit the spring branch.

Lecture and practice materials for each week are in ./week* folders. You can complete all asignments locally or in google colab (see readme files in week*)

General info

• 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 or ask around in the chat.

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
  • Homework 2 is out!
  • Seminar: PyTorch basics

• week03 Convolutional neural networks

  • Lecture: Computer vision tasks, Convolution and Pooling layers, ConvNet architectures, Data Augmentation
  • Seminar: Training your first ConvNet

(to be updated)

Contributors & course staff

Course materials by

• Victor Lempitsky - main track lecture videos (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
• many bugs were fixed and course materials were improved by students and volunteers, see PR authorship