Глубинное обучение (курс лекций)/2020
Материал из MachineLearning.
(Различия между версиями)
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| 02 Oct. 2020 || align="center"| 4 || Semantic image segmentation || [https://yadi.sk/d/jel16JzCmHLgBQ Presentation (pdf)]<br>[https://portrait.nizhib.ai/ Portrait Demo] ([https://github.com/nizhib/portrait-demo source]) | | 02 Oct. 2020 || align="center"| 4 || Semantic image segmentation || [https://yadi.sk/d/jel16JzCmHLgBQ Presentation (pdf)]<br>[https://portrait.nizhib.ai/ Portrait Demo] ([https://github.com/nizhib/portrait-demo source]) | ||
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| + | | 16 Sep. 2020 || align="center"| 3 || Neural style transfer. || [https://yadi.sk/i/Hp9wbpaIEHz_pw Presentation] | ||
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Версия 15:23, 19 октября 2020
This is an introductory course on deep learning models and their application for solving different applied problems of image and text analysis.
Instructors: Dmitry Kropotov, Victor Kitov, Nadezhda Chirkova, Oleg Ivanov and Evgeny Nizhibitsky.
The timetable in Autumn 2020: Fridays, lectures begin at 10-30, seminars begin at 12-15, zoom-link
Lectures and seminars video recordings: link
Anytask invite code: ldQ0L2R
Course chat in Telegram: link
Rules and grades
TBA
Lectures and seminars
| Date | No. | Topic | Materials |
|---|---|---|---|
| 11 Sep. 2020 | 1 | Introduction. Fully-connected networks. | |
| Matrix calculus, automatic differentiation. | Synopsis | ||
| 18 Sep. 2020 | 2 | Stochastic optimization for neural networks, drop out, batch normalization. | |
| Convolutional neural networks, basic architectures. | Presentation | ||
| 25 Sep. 2020 | 3 | Pytorch and implementation of convolutional neural networks. | ipynb 1 ipynb 2 |
| 02 Oct. 2020 | 4 | Semantic image segmentation | Presentation (pdf) Portrait Demo (source) |
| 16 Sep. 2020 | 3 | Neural style transfer. | Presentation |
