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fall22
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Deep learning course

This repo supplements Deep Learning course taught at YSDA and HSE @fall'22. For previous iteration visit the fall21 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

Contributors & course staff

Course materials and teaching performed by