Глубинное обучение (курс лекций)/2020
Материал из MachineLearning.
(Различия между версиями)
(Новая: __NOTOC__ This is an introductory course on deep learning models and their application for solving different applied problems of image and text analysis. '''Instructors''': [[Участ...) |
(→Lectures and seminars) |
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(9 промежуточных версий не показаны.) | |||
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Anytask invite code: ldQ0L2R | Anytask invite code: ldQ0L2R | ||
- | + | Course chat in Telegram: [https://t.me/joinchat/FIB6dhxS8U6wRFI-TDMNSA link] | |
== Rules and grades == | == Rules and grades == | ||
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!Date !! No. !! Topic !! Materials | !Date !! No. !! Topic !! Materials | ||
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- | | | + | | rowspan="2"|11 Sep. 2020 || rowspan="2" align="center"| 1 || Introduction. Fully-connected networks. || |
+ | |- | ||
+ | | Matrix calculus, automatic differentiation. || [https://drive.google.com/file/d/1Yu790uIPyxp9JIyysxfJDor_LJQu83gQ/view?usp=sharing Synopsis] | ||
+ | |- | ||
+ | | rowspan="2"|18 Sep. 2020 || rowspan="2" align="center"| 2 || Stochastic optimization for neural networks, drop out, batch normalization. || | ||
+ | |- | ||
+ | | Convolutional neural networks, basic architectures. || [https://drive.google.com/file/d/1uSVdPsn5wznk510gS9N1K9DXITpxNFXt/view?usp=sharing Presentation] | ||
+ | |- | ||
+ | | 25 Sep. 2020 || align="center"| 3 || Pytorch and implementation of convolutional neural networks. || [https://github.com/nadiinchi/dl_labs/blob/master/lab_cnn_english.ipynb ipynb 1]<br> [https://github.com/nadiinchi/dl_labs/blob/master/loss_surfaces_lab/lab_loss_surfaces.ipynb ipynb 2]<br> | ||
+ | [https://github.com/nadiinchi/dl_labs/blob/master/lab_pytorch.ipynb ipynb 3] | ||
+ | |- | ||
+ | | 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]) | ||
+ | |- | ||
+ | | 09 Oct. 2020 || align="center"| 5 || Object detection || [https://yadi.sk/i/vmJJgDAAvtY6Pw Presentation (pdf)]<br>[https://yadi.sk/i/5gLFLx1R7Qfjjg DS Bowl 2018 (pdf)] | ||
+ | |- | ||
+ | | 16 Oct. 2020 || align="center"| 6 || Neural style transfer. || [https://yadi.sk/i/Hp9wbpaIEHz_pw Presentation] | ||
+ | |- | ||
+ | | 23 Oct. 2020 || align="center"| 7 || Recurrent neural networks. || [https://drive.google.com/file/d/1KvSzzctOjRhYwJH_9LJJeZhMp4USTcDV/view?usp=sharing Presentation] | ||
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== Arxiv == | == Arxiv == |
Версия 08:51, 27 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) |
09 Oct. 2020 | 5 | Object detection | Presentation (pdf) DS Bowl 2018 (pdf) |
16 Oct. 2020 | 6 | Neural style transfer. | Presentation |
23 Oct. 2020 | 7 | Recurrent neural networks. | Presentation |