practical_dl's Introduction

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

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