Глубинное обучение (курс лекций)/2019

Материал из MachineLearning.

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This is an introductory course on deep learning models and their application for solving different problems of image and text analysis.

Instructors: Dmitry Kropotov, Victor Kitov, Nadezhda Chirkova, Oleg Ivanov and Evgeny Nizhibitsky.

E-mail for questions: bayesml@gmail.com. Please include in subject the tag [CMC DL19].

The timetable in Spring 2019: Fridays, most lectures begin at 14-35, seminars begin at 16-20. Exact place and time are given in tables below.

Link to a chat

Exam

The final exam is scheduled on 14th of June, r.613, start at 13-00. The exam will be organized in English.

Exam questions

Rules and grades

We have 5 practical assignments during the course. For each assignment, a student may get up to 10 points + possibly bonus points. A student is allowed to upload his fulfilled assignment during one week after deadline with grade reduction of 0.2 points per day. All assignments are prepared in English.

Also each student may give a small 10-minutes talk in English on some recent DL paper. For this talk a student may get up to 5 points.

The total grade for the course is calculated as follows: Round-up (0.3*<Exam_grade> + 0.7*<Semester_grade>), where <Semester_grade> = min(10, (<Assignments_total_grade> + <Talk_grade>) / 5.5), <Exam_grade> is a grade for the final exam (up to 10 points).

Final grade Total grade Necessary conditions
5 >=8 all practical assignments are done, exam grade >= 6 and oral talk is given
4 >=6 4 practical assignments are done, exam grade >= 4 and oral talk is given
3 >=4 3 practical assignments are done, exam grade >= 4

Practical assignments

Practical assignments are provided on course page in anytask.org. Invite code: bgvpqVE

Lectures

Date No. Place and time Topic Materials
15 Feb. 2019 1 r.685, 14-35 Introduction. Automatic differentiation.
22 Feb. 2019 2 r.685, 14-35 Optimization and regularization methods for neural networks

DropOut
Batch Normalization
Glorot initialization
ADAM optimizer

01 Mar. 2019 3 r.526b, 14-35 Convolutional neural networks for image classification problem

Slides (pptx)

15 Mar. 2019 4 r.526b, 16-20 Convolutional neural networks for image segmentation problem Slides (pdf), DS Bowl 2018 (pdf)
22 Mar. 2019 5 r.526b, 14-35 Object detection and localization on images Slides (pdf)
29 Mar. 2019 6 r.523 (instead of r.645), 12-50 Image style transfer Main models
Enhancements 1
Enhancements 2
Multi-style online models
Patch-based style transfer
05 Apr. 2019 7 r.526b, 14-35 Recurrent neural networks Slides (pdf)
12 Apr. 2019 8 r.526b, 14-35 Attention mechanism

Slides (pptx) Slides 2 (pdf)

19 Apr. 2019 9 r.526b, 14-35 Generative adversarial networks Slides (pdf)
17 May 2019 10 r.526b, 14-35 Students' presentations

Seminars

Date No. Place and time Topic Need laptops Materials
15 Feb. 2019 1 r.685, 16-20 Automatic differentiation. No Notes on backprop
22 Feb. 2019 2 r.685, 16-20 Introduction to Azure and Pytorch Yes Notebook
01 Mar. 2019 3 r.526b, 16-20 Convolutional neural networks for MNIST Yes
22 Mar. 2019 4 r.526b, 16-20 Semantic segmentation applications No Notebook, Portrait Demo
29 Mar. 2019 5 r.526b, 14-35 Image style transfer No
05 Apr. 2019 6 r.526b, 16-20 Recurrent neural networks Yes
12 Apr. 2019 7 r.526b, 16-20 Attention mechanism Yes
19 Apr. 2019 8 r.526b, 16-20 Generative adversarial networks No
17 May 2019 9 r.526b, 16-20 Students' presentations No

Arxiv

2017

2016

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