The lectures are suitable. Andrew, as always, is very clear and goes into necessary detail on each topic.

The programming assignments have adequate assistance and hints, except for the final week on Transformers.

The NLP Section of this course is quite difficult to understand(The Notations are quite confusing as well as prior knowledge is required to understand) but other than that RNN, GRU, LTSM are explained clearly.

Week1 lessons are a little complex than the previous classes and there are gaps between ground-up python version and keras version of LSTM model. Keras will need to be taught a bit more in detail to follow up.

This is by far the hardest course in the specialization. But it was explained well. My only complain is there were errors in the first programming exercise. All in all, I learned a lot in this specialization.

Great content! I really love Andrew's teaching style. (1 star deduction for some programming assignments where I spent time debugging but it turned out that the point deduction was due to the grading system.)

Great course, but the transformers section was much, much more difficult than the rest of the material. More time, lectures and exercises should be given to give us intuition about these very alien concepts.

Overall course content is great but I feel like improvements can be made on the lab exercises to give students concrete understanding along with hands-on experience. A great job by the deeplearning.AI team.

The programming exercises are a little heavy in this course where we need to load and re-load for them to give correct output even if the code had been correct all throughout. Otherwise, the course is great.

4 stars and not 5 stars because the course is shorter than the others and it feels like an exemple in classical forecasting is lacking (sales, time series...).

Really interesting but may be too focus on NLP.

It's an excellent course! I will give it 5 stars if it could offer more interesting and meaningful assignments(Not offend, but it a little too easy and the assignments are not very related to the real work).

The course should contain more explanation about natural language processing like tf-idf,lemmatization,stemming,dialog flow. Although i got a good explanation of working of RNNs,LSTM and machine translation

Great course to start off with sequence model. The programming exercises were in depth and deliver a great learning experience. Would love to see more of sequence literature in the course's future versions.

Again a brilliant course from Andrew NG, but though and dense this time. In order to grasp the meanings videos and lectures need to be revised a few times. Also, get some extra info elsewhere does not hurt.

Excellent presentation, and interesting assignments. One star dropped because a couple of technical issues with the assignment material (typos in the mathematical formulas / expected results here and there)

The contents are great as always. However, the server is not reliable. Once, the grader is down and you can't submit homework. For another time, the connection is lost and all the changes made are lost.

Great course, but I felt the gap between the very high level lectures and very low level labs was a bit too wide. I had some issues with the autograder and losing progress in the notebook between sessions.

Great course. The only frustrating part are the programming assignments as it is very cumbersome to have to go thru the discussion forums to resolve issues. Tough to find helpful insights thru the forums.

Andrew Ng is perfect like everytime. I didn't have any issue with programming exercises unlike other comment so probably it fixed. I wish course to be more detailed especially training word vectors part.

Very well produced and explained. In my case, the nature of the Sequence Model makes understanding the concepts and finishing the assignment more challenging than other segments of the specialization.

The course is very short when compared to first 3 courses in this series. It would have been better if more explanation and shorter assignments were provided in the initial weeks and increase the pace.

The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!

The material itself is very informative and useful. But I have to give "just" 4 stars because, the training videos have to be edited better and there were a few mistakes in the programing exercises.

Please work on getting the notebooks to work properly. Also very bummed that after canceling my subscription, I won't have access to my homeworks. You guys should give us lifelong access - we paid!

Could have been more polished like the earlier courses in the deep learning specialization. Particularly the programming exercises could have benefitted from more comments like in earlier courses.

A general overview into the power of sequence models. There is no rigorous mathematics here so most of what students can learn is high-level implementation and intuition about the various models.