The course content is pretty good for breadth. However, it falls short in going into depth. Assignments need to be more open-ended and probably a bit more involved. It appears that we are cutting and pasting code that is already written in comments.

This course presents an interesting review of several strategies that are part of the state of the art. However, it is impossible to assimilate how they work in the time devoted to each one. The "fill in the blanks" exercises do not help much.

This course gives a nice overview of sequence models. If it is true that I do not have an engineering background, I felt it got sometimes a little bit too abstract as compared to other courses of the specialisation. However, I recommend it.

I would advise giving more tutorials about TensorFlow and Keras. Those are your main tools and eventually, in many cases we were only required to complete the gaps which don't give you a true understanding of how to use those frameworks.

The programming exercises can be better polished, there was quite a few errors that caused unnecessary confusion to the students. Many times, I felt like I was fighting with the Keras/Tensorflow API rather than solving a ML problem.

Courses had some issues with the grader, and there were some instances where the expected output in the assignment didn't match the actual output, despite it being correct.

See forums for a range of complaints on the matter.

Need more detailed explanation and programming assignments are way too easy.I would suggest to make advanced courses for people to improve their knowledge keeping all this courses also considering not all feel it very easy.

In comparison with other courses in this specialisation a lot of assignments were poor quality - vague descriptions and code logic (especially week1, asign 2 & 3) or just broken (last week3 assignment)

With respect to the others, this one seems to be prepared almost in a hurry and the learning curve is very steep and sometimes the programming assignment don't have a nice progression as the others.

Videos were really informative and were equally interesting, but I believe that the programming assignments lacked a bit in clarity. The instructions were really unclear, it could have been better

Quite a few bugs or abstractions in this course, in comparison to the others the projects feel a bit rushed and pushed together. Andrews's explanations and video lectures were still great though.

this course is the most difficult in deep learning specification, but i think Andrew NG should design more homework for word embeddings and bidirectional rnn, i do not understand how it works yet

The course was great. However, coming from finance I was also hoping to see some examples which use time series so I can get a picture of how I can extend this knowledge to my specific domain.

The module was really good in explaining the concepts, but there wasn't any deep dive on the equations and mathematics behind with the results of making the code assignment harder to achieve.

I gave 5 stars to other courses in this series but this one doesn't deserve 5 stars. There were many typos and bugs in the assignments compared to the other courses of the specialization.

Video Lectures were excellent.

Assignments, however, were buggy and spoonfed you too much. I completed them, but on more than one occasion, I had no idea what the code was actually doing.

Boring course:

Short videos instead of lections.

Copy-paste task instead of exercises.

Loading pretrained models instead of training.

Good full keras tutorial will be ten times more useful.

The lectures were not as good as the previous andrew ng. courses, and the exercises were quite bad in all honesty.

I do appreciate what I have learned, as the lectures WERE clear enough.

Just one observation, the general view I have from this course is very positive but compared to the other courses the program exercises in this course are more difficult to understand.

A good introduction in Sequence models. Unluckily the programming assignments are more of a guess work then real knowledge as there are too few possibilities to check the own code.

Despite being a great introduction to many NLP concepts, the programming exercises are a bit too much "fill-in-the-blanks" and not challenging enough. It is still a great course !

Course contents were very good, but the assignments could have been improved a lot. Couldn't get much value out of the assignments rather than blindly following the instructions.

The class covered too less on the Sequence models such as LSTM and GRU. It has too much domain knowledge related videos. Also, it does not have any video related to Time Series.

The videos are good, however the assignments are too long with sooooo many text inside. You have to make it more efficient somehow.

But thank you anyway for the great content!

Assignments in this course are relatively less organized. You have to read a lot of description first and then code and have to go back/forth between code and description.