For me, there was a large gap on time between when course 4 and 5 were offered (months). This unfortunately was enough for me to forget everything I learned about Keras.

Of course, this course assumes you know Keras so I was behind for the labs

Material is interesting though.

Too many thing introduced in programming assignments without explanation. Why the high dropout values? Why sometimes one dropout layer, sometimes two? Many things are just given as a formula, and not explained in a way that would let me make my own network for my own problem.

Had to rate this lower due to problem with the final assignment. Submission and saving situation was a nightmare, I had to redo my work several times. Please fix this, it's a real downer at the end of the course. Otherwise, content stellar as always.

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.

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.

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.

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

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.

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.

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 !

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.

I believe that the course needs more time allocated to the incremental teaching of this rather large subject area with varied applications. Just needs to be a better way.

The course was especially interesting in week 2 and 3, but the assignments for week 1 were confusing and sometimes it was hard to follow where the logic was coming from.

Practical Assignments with Keras wasn't motivating. I spend more time to deep into the Keras rather than into the course topic. I prefer them to be using TF or Python.

The supports in keras programming was so poor, that I could not quite understand each step. And the server was horrible, always got 405 response when saving my codes.

The lecture was great but the errors in the programming assignment (especially in formal-typed formulas) really wasted a lot of time and make me confusing at first.

The ideas presented here were clear, however I found the programming assignments non-intuitive and not practical. I spent on them way more time than I wish i had.

I enjoyed all the courses, from my personal point of view this course was not that fun as the other courses. Except for the trigger assignment it was awesome!