Глубинное обучение (курс лекций)/2020

Материал из MachineLearning.

Перейти к: навигация, поиск

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
27 Nov. 2020 12 Variational Autoencoders
04 Dec. 2020 13 Reparameterization methods

Arxiv

2019

2017

2016

Личные инструменты