Глубинное обучение (курс лекций)/2020
Материал из MachineLearning.
(Различия между версиями)
(→Lectures and seminars) |
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!Date !! No. !! Topic !! Materials | !Date !! No. !! Topic !! Materials | ||
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| - | | rowspan="2"|11 Sep. 2020 || rowspan="2"|1 || Introduction. Fully-connected networks. || | + | | rowspan="2"|11 Sep. 2020 || rowspan="2" align="center"| 1 || Introduction. Fully-connected networks. || |
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| Matrix calculus, automatic differentiation. || [https://drive.google.com/file/d/1Yu790uIPyxp9JIyysxfJDor_LJQu83gQ/view?usp=sharing Synopsis] | | Matrix calculus, automatic differentiation. || [https://drive.google.com/file/d/1Yu790uIPyxp9JIyysxfJDor_LJQu83gQ/view?usp=sharing Synopsis] | ||
|- | |- | ||
| - | | rowspan="2"|18 Sep. 2020 || rowspan="2"|2 || Stochastic optimization for neural networks, drop out, batch normalization. || | + | | rowspan="2"|18 Sep. 2020 || rowspan="2" align="center"| 2 || Stochastic optimization for neural networks, drop out, batch normalization. || |
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| Convolutional neural networks, basic architectures. || [https://drive.google.com/file/d/1uSVdPsn5wznk510gS9N1K9DXITpxNFXt/view?usp=sharing Presentation] | | Convolutional neural networks, basic architectures. || [https://drive.google.com/file/d/1uSVdPsn5wznk510gS9N1K9DXITpxNFXt/view?usp=sharing Presentation] | ||
|- | |- | ||
| - | | 25 Sep. 2020 || 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> | + | | 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] | [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]) | ||
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== Arxiv == | == Arxiv == | ||
Версия 21:56, 4 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) |
