Глубинное обучение (курс лекций)/2019

Материал из MachineLearning.

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This is introductory course on deep learning models and their application for solving different problems of image and text analysis.
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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''': [[Участник:Kropotov|Dmitry Kropotov]], [[Участник:Victor Kitov|Victor Kitov]], Nadezhda Chirkova, Oleg Ivanov and Evgeny Nizhibitsky.
'''Instructors''': [[Участник:Kropotov|Dmitry Kropotov]], [[Участник:Victor Kitov|Victor Kitov]], Nadezhda Chirkova, Oleg Ivanov and Evgeny Nizhibitsky.
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E-mail for questions: ''bayesml@gmail.com''. Please include in subject the tag [CMC DL19].
E-mail for questions: ''bayesml@gmail.com''. Please include in subject the tag [CMC DL19].
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The timetable in Spring 2019: Fridays, room 685, lectures begin at 14-35, seminars begin at 16-20.
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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.
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[https://t.me/joinchat/DEBCqg_Y08322lq6WRqONg Link to a chat]
== Announcements ==
== Announcements ==
== Rules and grades ==
== Rules and grades ==
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We have 5 practical assignments during the course. For each assignment a student may get up to 5 points. A student is allowed to upload his fulfilled assignment during one week after deadline with grade reduction of 0.1 points per day.
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We have 5 practical assignments during the course. For each assignment, a student may get up to 5 points. A student is allowed to upload his fulfilled assignment during one week after deadline with grade reduction of 0.1 points per day. All assignments are prepared in English.
-
The final grade for the course is calculated as follows: Round-up (0.3*<Exam_grade> + 0.7*<Semester_grade>). For the final grade 5 it is necessary to fulfill all practical assignments and get >= 4 exam grade. For the final grade 4 it necessary to fulfill at least 4 practical assignments and get >= 3 exam grade. For the final grade 3 it is necessary for fulfill at least 3 practial assignments and get >=3 exam grade.
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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 final grade for the course is calculated as follows: Round-up (0.3*<Exam_grade> + 0.7*<Semester_grade>), where <Semester_grade> = <Assignments_total_grade> + <Talk_grade>. For the final grade 5 it is necessary to fulfill all practical assignments, get >= 4 exam grade and make an oral talk. For the final grade 4 it is necessary to fulfill at least 4 practical assignments, get >= 3 exam grade and make an oral talk. For the final grade 3 it is necessary to fulfill at least 3 practical assignments and get >=3 exam grade.
== Practical assignments ==
== Practical assignments ==
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{| class="standard"
{| class="standard"
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!Date !! No. !! Topic !! Materials
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!Date !! No. !! Place and time !! Topic !! Materials
|-
|-
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| 15&nbsp;Feb.&nbsp;2019 || align="center"|1 || Introduction. Automatic differentiation. ||
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| 15&nbsp;Feb.&nbsp;2019 || align="center"|1 || r.685, 14-35 || Introduction. Automatic differentiation. ||
|-
|-
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| 22&nbsp;Feb.&nbsp;2019 || align="center"|2 || Optimization and regularization methods for neural networks ||
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| 22&nbsp;Feb.&nbsp;2019 || align="center"|2 || r.685, 14-35 || Optimization and regularization methods for neural networks ||
|-
|-
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| 01&nbsp;Mar.&nbsp;2019 || align="center"|3 || Convolutional neural networks for image classification problem ||
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| 01&nbsp;Mar.&nbsp;2019 || align="center"|3 || r.526b, 14-35 || Convolutional neural networks for image classification problem ||
|-
|-
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| 15&nbsp;Mar.&nbsp;2019 || align="center"|4 || Convolutional neural networks for image segmentation problem ||
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| 15&nbsp;Mar.&nbsp;2019 || align="center"|4 || r.526b, 16-20 || Convolutional neural networks for image segmentation problem || [https://yadi.sk/i/xkw00-mr-Zk_Kw Slides (pdf)], [https://yadi.sk/i/n9O_1RP3QxGb8A DS Bowl 2018 (pdf)]
|-
|-
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| 22&nbsp;Mar.&nbsp;2019 || align="center"|5 || Object detection and localization on images ||
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| 22&nbsp;Mar.&nbsp;2019 || align="center"|5 || r.526b, 14-35 || Object detection and localization on images || [https://yadi.sk/i/_WDz9dcyvwCDIA Slides (pdf)]
|-
|-
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| 29&nbsp;Mar.&nbsp;2019 || align="center"|6 || Image style transfer ||
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| 29&nbsp;Mar.&nbsp;2019 || align="center"|6 || r.612, 8-45 || Image style transfer ||
|-
|-
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| 05&nbsp;Apr.&nbsp;2019 || align="center"|7 || Recurrent neural networks ||
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| 05&nbsp;Apr.&nbsp;2019 || align="center"|7 || r.526b, 14-35 || Recurrent neural networks ||
|-
|-
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| 12&nbsp;Apr.&nbsp;2019 || align="center"|8 || Attention mechanism ||
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| 12&nbsp;Apr.&nbsp;2019 || align="center"|8 || r.526b, 14-35 || Attention mechanism ||
|-
|-
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| 19&nbsp;Apr.&nbsp;2019 || align="center"|9 || Generative adversarial networks ||
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| 19&nbsp;Apr.&nbsp;2019 || align="center"|9 || r.526b, 14-35 || Generative adversarial networks ||
|-
|-
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| 26&nbsp;Apr.&nbsp;2019 || align="center"|10 || Riemannian optimization ||
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| 26&nbsp;Apr.&nbsp;2019 || align="center"|10 || r.526b, 14-35 || Riemannian optimization ||
|-
|-
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| 17&nbsp;May&nbsp;2019 || align="center"|11 || ||
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| 17&nbsp;May&nbsp;2019 || align="center"|11 || r.526b, 14-35 || Students' presentations ||
|-
|-
|}
|}
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{| class="standard"
{| class="standard"
-
!Date !! No. !! Topic !! Materials
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!Date !! No. !! Place and time !! Topic !! Need laptops !! Materials
-
|-
+
-
| 15&nbsp;Feb.&nbsp;2019 || align="center"|1 || Automatic differentiation. ||
+
-
|-
+
-
| 22&nbsp;Feb.&nbsp;2019 || align="center"|2 || Introduction to Azure and Pytorch ||
+
|-
|-
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| 01&nbsp;Mar.&nbsp;2019 || align="center"|3 || Convolutional neural networks for MNIST ||
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| 15&nbsp;Feb.&nbsp;2019 || align="center"|1 || r.685, 16-20 || Automatic differentiation. || No || [https://www.cs.ox.ac.uk/files/723/NA-08-01.pdf Notes on backprop]
|-
|-
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| 15&nbsp;Mar.&nbsp;2019 || align="center"|4 || Deep learning contests ||
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| 22&nbsp;Feb.&nbsp;2019 || align="center"|2 || r.685, 16-20 || Introduction to Azure and Pytorch || Yes || [https://github.com/nadiinchi/dl_labs/blob/master/lab_pytorch.ipynb Notebook]
|-
|-
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| 22&nbsp;Mar.&nbsp;2019 || align="center"|5 || Face recognition ||
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| 01&nbsp;Mar.&nbsp;2019 || align="center"|3 || r.526b, 16-20 || Convolutional neural networks for MNIST || Yes ||
|-
|-
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| 29&nbsp;Mar.&nbsp;2019 || align="center"|6 || Image style transfer ||
+
| 22&nbsp;Mar.&nbsp;2019 || align="center"|4 || r.526b, 16-20 || Semantic segmentation applications || No || [https://github.com/nadiinchi/dl_labs/blob/master/lab_semseg_en.ipynb Notebook], [https://github.com/nizhib/portrait-demo Portrait Demo]
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|-
 +
| 29&nbsp;Mar.&nbsp;2019 || align="center"|5 || r.526b, 14-35 || Image style transfer || No ||
|-
|-
-
| 05&nbsp;Apr.&nbsp;2019 || align="center"|7 || Recurrent neural networks ||
+
| 05&nbsp;Apr.&nbsp;2019 || align="center"|6 || r.526b, 16-20 || Recurrent neural networks || Yes ||
|-
|-
-
| 12&nbsp;Apr.&nbsp;2019 || align="center"|8 || Attention mechanism ||
+
| 12&nbsp;Apr.&nbsp;2019 || align="center"|7 || r.526b, 16-20 || Attention mechanism || Yes ||
|-
|-
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| 19&nbsp;Apr.&nbsp;2019 || align="center"|9 || Generative adversarial networks ||
+
| 19&nbsp;Apr.&nbsp;2019 || align="center"|8 || r.526b, 16-20 || Generative adversarial networks || No ||
|-
|-
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| 26&nbsp;Apr.&nbsp;2019 || align="center"|10 || Riemannian optimization ||
+
| 26&nbsp;Apr.&nbsp;2019 || align="center"|9 || r.526b, 16-20 || Riemannian optimization || No ||
|-
|-
-
| 17&nbsp;May&nbsp;2019 || align="center"|11 || ||
+
| 17&nbsp;May&nbsp;2019 || align="center"|10 || r.526b, 16-20 || Students' presentations || No ||
|-
|-
|}
|}

Версия 19:54, 24 марта 2019

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

Announcements

Rules and grades

We have 5 practical assignments during the course. For each assignment, a student may get up to 5 points. A student is allowed to upload his fulfilled assignment during one week after deadline with grade reduction of 0.1 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 final grade for the course is calculated as follows: Round-up (0.3*<Exam_grade> + 0.7*<Semester_grade>), where <Semester_grade> = <Assignments_total_grade> + <Talk_grade>. For the final grade 5 it is necessary to fulfill all practical assignments, get >= 4 exam grade and make an oral talk. For the final grade 4 it is necessary to fulfill at least 4 practical assignments, get >= 3 exam grade and make an oral talk. For the final grade 3 it is necessary to fulfill at least 3 practical assignments and get >=3 exam grade.

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
01 Mar. 2019 3 r.526b, 14-35 Convolutional neural networks for image classification problem
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.612, 8-45 Image style transfer
05 Apr. 2019 7 r.526b, 14-35 Recurrent neural networks
12 Apr. 2019 8 r.526b, 14-35 Attention mechanism
19 Apr. 2019 9 r.526b, 14-35 Generative adversarial networks
26 Apr. 2019 10 r.526b, 14-35 Riemannian optimization
17 May 2019 11 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
26 Apr. 2019 9 r.526b, 16-20 Riemannian optimization No
17 May 2019 10 r.526b, 16-20 Students' presentations No

Arxiv

2017

2016

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