Deep learning course
This repo supplements Deep Learning course taught @spring'23. For previous iteration visit the fall22 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
- 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