Great course, great activities and really good programming excercises.

I give it 3 stars because instructors let political views tainted week 3 videos and assignments of this course by introducing some techniques for 'debiasing' and making your neural networks more bias to gender equality political views. That has nothing to do with science.

I've got mixed feelings about the whole Specialization. Many very interesting topics, but on the other hands I don't feel like there's any takeaway knowledge for me. Until the very end I've been feeling completely lost in the exercices. I'm proud to have been able to hold on until the end but I'm not sure it's been an useful use of my time.

Really interesting course with fascinating applications. However, in terms of difficulty, it is a significant step up from all the previous courses. A lot of time is spent figuring out the syntax even though the concepts are crystal clear. ( Probably as it is a collaboration with NVIDIA). The programming assignments could be improved.

It´s a fairly good course, with lots of cool topics covered on it. My main complain would be that the subjects covered are dense enough to be arranged on a four or even five week course. Instead, for some reason, all the stuff has been squeezed within three weeks, which makes the lectures shallow and rather cryptic most of the time.

The course was great, as ever. But some of the programming exercises were very frustrating. Oscillating from very easy to very difficult, with some unclear (and sometimes erroneous) instructions. I felt this was in sharp contrast with the previous 4 courses of this specialisation, for which the course and exercises were perfect.

the course covered a lot of essentials and gave me a rough idea of how stuff NLP and sequence models work. Though at the same time the content often left me confused and overwhelmed. the Convolutional Networks course was far better.

Overall its great work and I am thankful for hard work put behind the complete specialization.

Great lectures, great teacher!

I would have given 5 stars but for the problems in the exercises / grader. Some problems that are know for weeks or even months are not resolved. This causes many wasted hours for many hundreds of students. Please solve this and make it a 5 star course.

Many thanks to Andrew Ng and the mentors!

The programming tasks were very simple. I doubt that you can really learn anything when you just need to copy the text from the task description and paste it. The content of the tasks was excellent, but the level of personal involvement was minimal.

In addition, the information in the course is already outdated.

The lectures were OK ... better LSTM tutorial by Chris Olah

The exercises really need some review ... very frustrating ... and not all that illuminating .

The course was a good intro to DNN ... but I think either replace Week 3 - Structuring ML Projects with a course on Keras ... or add a course just on Keras.

In my oppinion this course was too hard. I mean I could solve the assignments, but there was too much "magic" in those assignments. At least for me it was hard to develop enough intuition. But maybe its also due to the fact that I am more interessted in image based convnets where I have more background.

The course does not explain how to use Keras (it's assumed you've finished the previous course). What's more a lot of code parts is implemented in some difficult way (for loops instead of Python's builtins and idioms like any or list comprehensions). I'd love to see more materials on speech recognition.

I guess as the subject matter becomes more complex, more training is required on the underlying frameworks being used- Keras, TensorFlow etc. Did not feel that sufficient time was spent on understanding the underlying frameworks. Also the TA work is of spotty quality. But I love the way Andrew teaches.

The assignments were not good i think. Because they explained the consepts too long and complicated as like we've never seen these on lectures. I was waiting assignment to require more insight about architecture and less python programming knowledge. This comment is for week1 assignments in special.

Programming assignments were not to the level of the prior courses in the series. Should have more illustration of using Keras/Tensorflow. Assignments either were too spoon fed or there was too little reference information whereas prior courses had a good balance. Many of the keras links are dead.

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.

Of the courses in the specialization, this one seemed the least organized and rushed. Some of the assignments had some annoying auto-grader quirks that made troubleshooting a pain. Overall it is still worthwhile, just be ready to search forums for help during the assignments.

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.

The lessons are very good as always, but I'd like to be tested more in the programming exercises rather than literally being told what to do and then fill in missing parts of already completed code. Still super glad I took the specialisation, it has been extremely helpful.

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.

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 assignments are more like quests - most of the time is spent guessing what is required. The changes made require more programming skills rather than the understanding of ML principles.

The "Attention" topic was not in good detail.

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.