Глубинное обучение (курс лекций)/2019
Материал из MachineLearning.
Строка 4: | Строка 4: | ||
'''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. | ||
- | + | The timetable in Autumn 2019: Mondays, lectures begin at 10-30, seminars begin at 12-15, room 526b. | |
- | + | ||
- | The timetable in | + | |
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
== Rules and grades == | == Rules and grades == | ||
- | We have | + | We have several 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. | 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). | + | 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). | ||
{| class="standard" | {| class="standard" | ||
!Final grade !! Total grade !! Necessary conditions | !Final grade !! Total grade !! Necessary conditions | ||
Строка 30: | Строка 22: | ||
| 3 || >=4 || 3 practical assignments are done, exam grade >= 4 | | 3 || >=4 || 3 practical assignments are done, exam grade >= 4 | ||
|- | |- | ||
- | |} | + | |}--> |
== Practical assignments == | == Practical assignments == | ||
- | Practical assignments are provided on course page in ''anytask.org''. Invite code: | + | Practical assignments are provided on course page in ''anytask.org''. Invite code: ????? |
== Lectures == | == Lectures == | ||
{| class="standard" | {| class="standard" | ||
- | !Date !! No. | + | !Date !! No. !! Topic !! Materials |
|- | |- | ||
- | | | + | | 02 Sep. 2019 || align="center"|1 || Introduction. Fully-connected networks. Automatic differentiation. || |
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
|- | |- | ||
|} | |} | ||
Строка 72: | Строка 39: | ||
{| class="standard" | {| class="standard" | ||
- | !Date !! No. | + | !Date !! No. !! Topic !! Need laptops !! Materials |
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
- | + | ||
|- | |- | ||
- | | | + | | 9 Sep. 2019 || align="center"|1 || Introduction to Pytorch || Yes || |
|- | |- | ||
|} | |} |
Версия 01:08, 2 сентября 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.
The timetable in Autumn 2019: Mondays, lectures begin at 10-30, seminars begin at 12-15, room 526b.
Rules and grades
We have several 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>).
Practical assignments
Practical assignments are provided on course page in anytask.org. Invite code: ?????
Lectures
Date | No. | Topic | Materials |
---|---|---|---|
02 Sep. 2019 | 1 | Introduction. Fully-connected networks. Automatic differentiation. |
Seminars
Date | No. | Topic | Need laptops | Materials |
---|---|---|---|---|
9 Sep. 2019 | 1 | Introduction to Pytorch | Yes |