BEST

Nice

Done

good

cool

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nice

nice

Best

good

good

666

Wow

100

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Given the very high quality of Professor Andrew Ng's lectures, I wanted to give this five stars, and I have given his previous classes five stars for that reason. You truly do learn a lot from what he teaches!

Sadly I have to downrate this particular class due to huge technical problems with submitting Jupyter notebook based assignments. In particular, week 4's face recognition assignment was marred by several issues. For one thing, the grader would often crash and report technical issues instead of grading the assignment. For another thing, the grader would also take 30 to 60 minutes to run, which is far longer than it takes to actually run the Jupyter notebook itself! Finally, and quite seriously, in order to get 30/30 on the face recognition assignment of week 4, I had to submit an INCORRECT answer due to a bug in the grader itself. At least for me these are primarily week 4 issues.

This class is still a terrific value and a valuable course for anyone wishing to study deep learning, and I am planning to make good use of these lessons. But I do believe that especially for the face recognition assignment it would have behooved the developers to test the assignment thoroughly before making it available to students and also to correct the substantial quality problems (i.e. failing a correct answer and accepting a wrong answer along with grader crashes) involving the grader. I hope these programming assignment quality problems do not appear again in the fifth course on recurrent networks because I am very much looking forward to that particular course given some of the projects I work on!

I was torn on whether to give this three stars only or four stars but decided that given the overall learning value I am still giving it four stars. But again I do hope the developers are reading these reviews and also the discussion forums regarding the major quality issues involving the face recognition programming assignment. The other assignments were very good and did not pose such ridiculous issues (my experience only.)

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended