Chevron Left
Back to Deep Learning in Computer Vision

Learner Reviews & Feedback for Deep Learning in Computer Vision by National Research University Higher School of Economics

3.8
143 ratings
38 reviews

About the Course

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI. Do you have technical problems? Write to us: coursera@hse.ru...

Top reviews

SJ

Jun 12, 2018

Excellent course! Quiz questions are conceptual and challenging and assignments are pretty rigorous and 100% practical application oriented.

RR

Apr 19, 2019

Don't just read what's written on the projector. Try explaining it. And explain with code.

Filter by:

1 - 25 of 37 Reviews for Deep Learning in Computer Vision

By Rohan K

Jul 24, 2018

Very poor instructions in course, can' even understand a thing.

1.Tutor does not explain clearly what exactly is happening, just reads the formula on screen as it is.

2.What is kernel and why we use it. Also what happens exactly when we convolutionalize,image matrix. Nothingggg is explained.

3. How one should solve the maths in graded assignments, when can't even understand the math behind the technique.

4. Speaking accent of tutor is very bad, can't understand if captions are not enabled.

I have taken many courses on coursera, which were very informative and in-depth explanation, but this course is just like National research university has nothing to do with if students are actually learning or not. It is more like earning money only.

There are several other cons which I have not mentioned.Very very disappointed.

By Kocić O

Sep 20, 2018

Pros:

- excellent and challenging exercises

- relevant topics

Cons:

- poor feedback and course management

- lectures that do not teach you anything, it is more a taxonomy of what exists out there than explanation of anything

By

Nov 06, 2018

Lecturer had a difficult accent to understand. Briefly put up formulas and graphs but didn't explain them. No examples were worked through yet the quiz required this to be done. To understand this subject I will have to go away and study this from another source before I can understand it.

By Jakub B

Nov 23, 2018

Pros:

-thorough course material

-ambitious and interesting assignments

Cons:

-there isn't ANY assistance from the instructors or the TA. If you check TAs then you'll see that all of them written 0 posts on the forum.

-most assignments are severely underspecified. This makes them much harder than they should be, and it makes students spend hours on minutiae of preprocessing instead of more important stuff.

By Nafiseh S N

Oct 30, 2018

Worst presentation ever! Hard to follow, he reads from a text with no explanation. The worst! I also have to read the transcript to understand what he is saying.

By Rachepalli R R

Apr 19, 2019

Don't just read what's written on the projector. Try explaining it. And explain with code.

By Matt V

Sep 10, 2018

Some good topics and interesting homeworks, however, the lectures are rushed through without much explanation or examples, and there are many frustrating mistakes that belie the fact that this course is apparently not maintained and there is no support for students on the forums. Could be really good, but it's not there yet.

By Sahil J

Jun 12, 2018

Excellent course! Quiz questions are conceptual and challenging and assignments are pretty rigorous and 100% practical application oriented.

By Raymond P

Oct 02, 2018

Slides are not available, only the videos. Quiz questions are very unclear and ambiguous. Assignments have almost no direction and require many hours of commitment. Requires a significant amount of prior knowledge before taking this course. Would not recommend. Andrew Ng's deeplearning.ai specialization is much better.

By MASSON

May 15, 2019

LECTURES

-> Instructors read scripts during the whole course

-> No lecture slide (seriously ? thanks a lot for the effort)

-> Lectures are instructive even if you already know a bit about computer vision

-> Some topics and technical points are unclear, and quickly done with

ASSIGNMENTS

-> ALL assignments are peer graded, meaning you never know if you will get a grade before deadline

-> Forums and discussions are deserted, you won't find any help there

-> Instructors provide very little explanations and directions

-> Some assignments can be pretty tough to complete if you are not strongly familiar with CV

-> Some assignments are unrelated to lectures (Week1 Alignment, Week5 GAN)

Overall the course is really interesting and instructive, but it feels like it was designed in a rush.

You can look elsewhere for a good course about computer vision.

By Yury Z

Jun 20, 2018

Great content! Great assignments ideas. Problems with graders. Quizes and especially assignments, are loosely related to the lecture materials. The ability of authors to explain things is pretty week (it is not even close to Andrew Ng skills). The efforts necessary to complete assignments are ridiculously underestimated. You will need 10-20 hours or even more for each assignment, instead of 2hr specified. Total experience is very contradictory. I chose between 3 and 4. 3 is too low mark for these unique content, and creative assignment ideas, and I hope the assignment format could be improved in the feature, with not so many efforts (some tips, and intermediate assertions could help a lot). As for now, I could not recommend this course for wide audience, only for the most "desperate" students.

By 李朝辉

Dec 14, 2018

the descriptions of homeworks are sometimes ambiguous, students may spend too much time trying to understand. Also, I strongly recommend teachers can interactive with the PPT when lecturing, instead of reading the draft directly.

By Потапчук А А

Aug 04, 2018

Lectures are good (mostly). But home assignments are very bad. Some links for datasets are broken. Admins did not fix it after a month (I found a message about it on the forum)!!! There are a lot of mistakes in a code. So, I spent a huge amount of time just for fixing bugs. It was very painful.

I think, administrators should read comments sometimes and update repos.

Some home assignments are very cool. But, mostly it is not possible to run it on a low-class laptop.

Moreover, there are a lot of mistakes in quizzes that was not fixed for a long time! So, people tried a grid search for answers. I definetely recommend to administration start fixing this bugs. This course is very good, but all this bugs discourage any desire to pass it.

By Alex

Jul 06, 2018

Course is very raw. Number of mistakes, missing lectures, missing subtitles, missing slides.Quality of lectures, especially in the starting weeks. From my point of view, teachers just read subtitles under the camera, making strange pauses, which ruins understanding.Overwhelming tasks (taking more than person's 20h each) with too few help.And no answers / support from the instructors during the course at all. Which is most disappointing, because other things can be fixed / tuned somehow.I will argue anybody from taking this course, until it's reworked and staff started to participate in the forums.

By 杨伟

Apr 29, 2019

I am not sure how to rate this course exactly.

1, It is difficult to study. mainly because it just list most tech in computer vision but without detailed explanation.it is not better than reading the origin papers.

2, Some quizz are very confusing, Actually adding a little explanation will make the quizz more clear.

3, The assignments are not uniform. it did not give a uniform environments. you need setup the env by yourself.

4, But when you finished the course, you will learn a lot things.

By Arnaud R

Apr 10, 2019

Some good and challenging assignements but overall the material is delivered poorly. Slides/Videos are rushed through, quizzes are messy and there is barely any help given. I was excited to do the course as a followup from deeplearning.ai but it ended being a chore. I still learned useful skills and concepts overall but be warned.

By Artem E

Jul 03, 2018

Very bad course in quality of performance. The authors created it, but did not support it. Multiple errors, lack of help on the forum and the absence of correction of obvious problems (link to download the GAN dataset)

By Milos V

Feb 28, 2019

Computer vision is far the most difficult AI application, and for this the best course should be done to make ti clear. However, here we had awful lecturers (they are reading as robot!) and "hints" are often misleading or wrong... I gave 2 stars only because assignments were nicely connected, but that is all.

Message to the "AML-bosses": this course should not be in this specialization!!!

By Lukas K

Aug 03, 2019

The worst course I ever had on Coursera. To be honest I do thing, that it is ruining the whole specialization. None of the instructors is caring and it is seen everywhere. The sum of posts for all instructors and mentors on forum is 0, which means you can ask, but they do wont answer. All the assignment has to be reviewed by peers, because it seems that none of the author even cared about making the grader (even I do thing it is possible for most of the assignments). Quizzes are also somehow put together, but you wont get much from them.

Instructors are only reading and not explaining, so there is a lot of moments, where instructor just stop saying something because he is waiting for the text. Instructors even do not care about putting the slides for the course.

By Nandini

Apr 19, 2019

It was good

By Wei X

Aug 31, 2018

Nice introductory course. It include many background knowledge of computer vision before deeplearning and is important to know.

By Erik G

Jun 07, 2019

Some lectures aren't clearly structured. The homework assignments have downstream dependencies (week 5 depends on earlier weeks) which is not the best format IMO.

By Anmol G

May 02, 2019

The content was good, but there are several drawbacks too

1 Content not explained well

2 Assignments had mistakes (like broken links, wrong descriptions)

3 Minimal or Non-Existing support from the staff.

By Jiachen L

May 05, 2019

Course homework is actually not well designed for Coursera platform, some homework is not quite relevant to Deep Learning (i.e., HW1 and HW2) and some is very hard to complete (i.e., the last one, I tried on CPU for about 2 weeks). Also, the slides are not shared for this course in the series.

By William

Apr 22, 2019

The task is really practical and useful, but the courses content is too general and I hope for the future it could include some papers reading assignment