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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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

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.

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!!!

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)

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.

Not clear. And just focus on papers, not on basic structure or models.

No relation of videos and tasks

Worst course in the specialization.

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.

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.

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