The material was better in this course than the previous ones, but still lacking depth in my opinion. Also, no graded assignments?? So the focus is then only on the quizzes, and they are not even well done. From week to week the same questions are repeated and the quizzes don't even include code: How is this teaching code?

This course was quite sloppily presented and superficial overall. There were a couple of longstanding errors that have never been fixed (see the lengthy discussions in forums). One thing that annoyed me was that the important concept of stop-words was not discussed at all, yet it was required for the first assignment.

All assignments were optional - probably because of all the problems with the scoring system for the previous course. Quizzes often asked things about the dataset we used (eg IMDB) rather than testing that we were learning concepts. Very little meat to the course - mostly links to other resources.

After completing the Deep Learning specialization, which I really liked, I had high expectations for this one. Unfortunately it can not meet my expectations and was a dissapointment. Even if I try to see it objectively and ignore my high expectations, the quality of the exercises were very poor.

Overall the video material is fine. The assignments however are very unclear and contain bugs. The grader's test don't match the instructions. It's very frustrating that the assignments clearly haven't been given the same attention the rest of the course has been.

This course is a far cry from Andrew Ng's deep learning specialization and refers to Sequence Models from that specialization at the drop of a hat. In short, no use doing this one, unless you have done sequence models (course 5) of deep learning specialization.

No grader exercises,

sample code in the lectures isn't always updated and gives errors,

everything is a bit chaotic (eg order of sample code, sample code description, introduction to the topic is random; some random parts in the code).

The weekly exercises are not graded and the over all content quality of this course in comparison with the previous two in the specialization seems a bit poor and doesn't provide more learning on the topic.

cant fix kernel died, padahal sudah ke pusat bantuan dan forum diskusi tetap tidak bisa, alhasil kurang maksimal dalam mengerjakan assigment karena salah satu poinnya tidak bisa dirun, terimakasih

Not clearly explained and only using toy and irrelevant datasets, nothing realtime industry specific examples. Also, voice quality is very bad for this course.

I would call this entry/beginner level material. There arent any graded coding challenges, which is a shame. No complex topics are covered with this class.

It's a bit too basic and there are not many graded examples to work through like Andrew Ng's course. I feel it could have been more complete and in depth

Disappointing. Laurence much less able to explain NLP issues than CNN issues. Lots of problems with TF versions in Colabs wasted far too much time.

The quality of the audio recording is worse than courses before. The questions at the end of the chapters are also repetitive.

The given material is pretty nice, but I don't feel like I learned a lot. Important concepts weren't explained in depth.

didn't really feel like a strongly grasped the concept and needed more exercises also the lack of lessons notebooks.

Too short. Fine as introduction but not in depth course. No assignment except very shallow multiple choices tests.

some links are broken.

The last quiz (week4) not match to the lessons

couldn't run the lab excercizes

Not enough content; far too easy; the whole course should be one week of a good tensorflow course.

The audio quality is very bad! It should be improved so the content is audible to the user

Assignments often demanded Python knowledge and not the skills learned in the course!

There is no graded exercise and the class is outdated that some links do not work.

I wish assignments were provided the same way as in the previous two courses

Useful but shorter and easier than expected and not deep enough for me

It is too mechanical and reinforcement of concepts if very limited