Глубинное обучение (курс лекций)/2019
Материал из MachineLearning.
(→Rules and grades) |
|||
| Строка 1: | Строка 1: | ||
__NOTOC__ | __NOTOC__ | ||
| - | This is introductory course on deep learning models and their application for solving different problems of image and text analysis. | + | 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. | ||
| Строка 7: | Строка 7: | ||
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. | 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. | ||
| + | |||
| + | [https://t.me/joinchat/DEBCqg_Y08322lq6WRqONg Link to a chat] | ||
== Announcements == | == Announcements == | ||
== Rules and grades == | == 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. | + | 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. | 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. | ||
| Строка 52: | Строка 54: | ||
!Date !! No. !! Place and time !! Topic !! Need laptops !! Materials | !Date !! No. !! Place and time !! Topic !! Need laptops !! Materials | ||
|- | |- | ||
| - | | 15 Feb. 2019 || align="center"|1 || r.685, 16-20 || Automatic differentiation. || No || | + | | 15 Feb. 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] |
|- | |- | ||
| 22 Feb. 2019 || align="center"|2 || r.685, 16-20 || Introduction to Azure and Pytorch || Yes || | | 22 Feb. 2019 || align="center"|2 || r.685, 16-20 || Introduction to Azure and Pytorch || Yes || | ||
Версия 22:22, 16 февраля 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.
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.
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.
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.612, 8-45 | Convolutional neural networks for image classification problem | |
| 15 Mar. 2019 | 4 | r.526b, 14-35 | Convolutional neural networks for image segmentation problem | |
| 22 Mar. 2019 | 5 | r.526b, 14-35 | Object detection and localization on images | |
| 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 |
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 | |
| 01 Mar. 2019 | 3 | r.526b, 16-20 | Convolutional neural networks for MNIST | Yes | |
| 15 Mar. 2019 | 4 | r.526b, 16-20 | Deep learning contests | No | |
| 22 Mar. 2019 | 5 | r.526b, 16-20 | Face recognition | No | |
| 29 Mar. 2019 | 6 | r.526b, 14-35 | Image style transfer | No | |
| 05 Apr. 2019 | 7 | r.526b, 16-20 | Recurrent neural networks | Yes | |
| 12 Apr. 2019 | 8 | r.526b, 16-20 | Attention mechanism | Yes | |
| 19 Apr. 2019 | 9 | r.526b, 16-20 | Generative adversarial networks | No | |
| 26 Apr. 2019 | 10 | r.526b, 16-20 | Riemannian optimization | No | |
| 17 May 2019 | 11 | r.526b, 16-20 |
