Глубинное обучение (курс лекций)/2020

Материал из MachineLearning.

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(Lectures and seminars)
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!Date !! No. !! Topic !! Materials
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| rowspan="2"|11 Sep. 2020 || rowspan="2"|1 || Introduction. Fully-connected networks. ||
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| 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]
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| 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]
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| 25&nbsp;Sep.&nbsp;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>
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[https://github.com/nadiinchi/dl_labs/blob/master/lab_pytorch.ipynb ipynb 3]
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| 02&nbsp;Oct.&nbsp;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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| 09&nbsp;Oct.&nbsp;2020 || align="center"| 5 || Object detection. || [https://yadi.sk/i/vmJJgDAAvtY6Pw Presentation (pdf)]<br>[https://yadi.sk/i/5gLFLx1R7Qfjjg DS Bowl 2018 (pdf)]
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| 16&nbsp;Oct.&nbsp;2020 || align="center"| 6 || Neural style transfer. || [https://yadi.sk/i/Hp9wbpaIEHz_pw Presentation]
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| 23&nbsp;Oct.&nbsp;2020 || align="center"| 7 || Recurrent neural networks. || [https://drive.google.com/file/d/1KvSzzctOjRhYwJH_9LJJeZhMp4USTcDV/view?usp=sharing Presentation]
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| 30&nbsp;Oct.&nbsp;2020 || align="center"| 8 || Recurrent neural networks memory and attention mechanisms. ||
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| 06&nbsp;Nov.&nbsp;2020 || align="center"| 9 || Reinforcement learning. Q-learning. DQN model. ||
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| rowspan="2"|13&nbsp;Nov.&nbsp;2020 || rowspan="2" align="center"| 10 || Policy gradient in reinforcement learning. REINFORCE and A2C algorithms. ||
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| Reinforcement learning implementation and multi-armed bandits. || [https://github.com/nadiinchi/dl_labs/blob/master/lab_reinforcement_en.ipynb RL notebook]<br>[https://www.youtube.com/watch?v=kopoLzvh5jY Multi-Agent Hide and Seek video]<br>[https://github.com/nadiinchi/dl_labs/blob/master/lab_bandits.ipynb Bandits notebook]<br>[https://learnforeverlearn.com/bandits/ Bayesian Bandit Explorer]
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| 20&nbsp;Nov.&nbsp;2020 || align="center"| 11 || Generative adversarial networks. || [https://yadi.sk/i/wNmNOSipwhRbWQ Part1] [https://yadi.sk/i/s5goIhh_0WxLwg Part2]
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== Arxiv ==
== Arxiv ==

Версия 11:53, 24 ноября 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

ipynb 3

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
30 Oct. 2020 8 Recurrent neural networks memory and attention mechanisms.
06 Nov. 2020 9 Reinforcement learning. Q-learning. DQN model.
13 Nov. 2020 10 Policy gradient in reinforcement learning. REINFORCE and A2C algorithms.
Reinforcement learning implementation and multi-armed bandits. RL notebook
Multi-Agent Hide and Seek video
Bandits notebook
Bayesian Bandit Explorer
20 Nov. 2020 11 Generative adversarial networks. Part1 Part2

Arxiv

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