Глубинное обучение (курс лекций)/2019
Материал из MachineLearning.
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.
Exam
The final exam is scheduled on 14th of June, r.613, start at 13-00. The exam will be organized in English.
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 |
| 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.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 | |
| 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 |
