Глубинное обучение (курс лекций)/2020
Материал из MachineLearning.
(Различия между версиями)
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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] | ||
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| + | | rowspan="2"|18 Sep. 2020 || rowspan="2"|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] | ||
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Версия 14:20, 18 сентября 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 |
