Глубинное обучение (курс лекций)/2020
Материал из MachineLearning.
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| + | == Student presentations == | ||
| + | Each student may prepare a presentation on some recent DL topic. This activity is compulsory for the final course grade 5 and optional for all the other cases. Presentation must be in English, 10-minutes long and cover some papers from the last 3 years (2018, 2019 and 2020). Please register for particular talk on either 11th or 18th of December [https://docs.google.com/spreadsheets/d/1gBDNmhzJ5NTT1LCTPr089aetOBjHlCIri8OADPlsnmo/edit?usp=sharing here]. The maximum capacity for each of two days - 12 presentations. | ||
== Lectures and seminars == | == Lectures and seminars == | ||
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| 04 Dec. 2020 || align="center"| 13 || Reparameterization methods || | | 04 Dec. 2020 || align="center"| 13 || Reparameterization methods || | ||
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| + | | 11 Dec. 2020 || align="center"| 14 || Student presentations || | ||
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| + | | 18 Dec. 2020 || align="center"| 15 || Student presentations || | ||
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Версия 14:48, 4 декабря 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
Student presentations
Each student may prepare a presentation on some recent DL topic. This activity is compulsory for the final course grade 5 and optional for all the other cases. Presentation must be in English, 10-minutes long and cover some papers from the last 3 years (2018, 2019 and 2020). Please register for particular talk on either 11th or 18th of December here. The maximum capacity for each of two days - 12 presentations.
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 |
| 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 |
| 27 Nov. 2020 | 12 | Variational Autoencoders | |
| 04 Dec. 2020 | 13 | Reparameterization methods | |
| 11 Dec. 2020 | 14 | Student presentations | |
| 18 Dec. 2020 | 15 | Student presentations |
