Глубинное обучение (курс лекций)/2020

Материал из MachineLearning.

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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


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





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