This last course of the series while still being excellent, it had a few minor issues in the assignments and was quite hard compared to the previous four. Nevertheless, I still learned a lot from it and I am really grateful for it being available.

Very interesting and well taught course. The only disappointment is that it focuses almost completely on NLP. I would have much preferred working on other topics too, like for example time series with LSTM, which instead didn't even get mentioned.

You should try to leave access to the previous code I wrote in the previous weeks or help out a little in week2 exercise I really struggled to get some of the code done because I didn't have access to my previous notebooks because you locked them

Everything was great, however week 4 would benefit from spending a little more time in the lecture videos going over some of the details on the inputs and outputs and their dimensions and how they differ between the encoding and decoding stages.

The course is very interesting and well organized. Certainly the content is appropriate for the cost of the course. The only flaw, for me, is the lack of mathematical demonstrations, but I know it is impossible to cover this in a single course

This course helps you build the basics for natural language processing using deep learning methods.

The assignments at the end of every week test your understanding of the subject and improves your understanding of the topic. Highly Recommended

Good continuation of RNNs covering theory and Python exercises using a few algorithms and uses cases. I would love to see more content and more interesting examples to implement in Python. Still, this is a nice introduction to sequence models

Amazing information. My one complaint is that the assignments don't help you understand how to work with TensorFlow. Most of the time I would read the documentation on the functions and follow the syntax. Maybe it simply comes with practice.

I really liked it, however I don't feel like it really went into some of the more practicle issues with sequence models. I was left feeling like I wouldn't really know what to do in a situation where I had highly variable sequence lengths.

I preferred the first specialization courses. The explanations are not so clear as the explanations in the first courses. I will make the NLP specialization to have a better understanding. Anyway, I recommend the specialization. Very good!

It is a very good course. Andrew Ng explanations are very clear and easy to understand with a lot of good examples. On the other hand there are some confusions or errors in the backpropagation part of the programming assignment about LSTM.

I am rating this course 4 because It doesn't give any guidance about future career paths and next things to learn. The explanations are very good. I understood complex things like GRU, LSTM, Bidirectional RNN, attention model very well.

While a great introduction on RNNs, I felt there could be another week of lectures given the complexity of the algorithms being explained. Likewise, the programming exercises felt unpolished in some parts, like in the expected outputs.

Very good. To make it perfect, would have liked it for the Assignments to have less bugs (cf. LSTM backprop), and a longer course with extra weeks to present LSTM in the context of prediction (finance, weather, pattern recognition,...)

Great course! only negative is that problems would really hold your hand. I don't think there is any way I would pass a whiteboard test on any of this (then again a course to get me to that level would have to be double this length).

The course covers one of the most influential developments in deep learning in recent times, and does so in a thorough way, introducing majority of the relevant mathematics and methods necessary to build a variety of sequence models.

The course is really good if you want to get a good understanding on the basics of deep learning. It would have been great if the course had more hand's on assignments than fill in the blanks kind of assignments in ipython notebook.

I learnt a lot from this course and the whole specialization. I am grateful to the mentors and instructors. If coursera gives me opportunity I can also be mentor for the specialization to help the newcomers through the assignments.

During the first week, I think that a deeper explanation of the matrices' dimensions throughout the NNs should be given. Indeed, this would be helpful to understand some concepts.

Apart from that, it was an awesoome course, thanks!

The course was good except first week. I did not learn as much as I would like from the programming exercises of week 1. It could be nice to have 4 weeks instead of 3 for this course. Taking more time to cover the week 1 material.

Everything is perfect, the teaching is excellent, the only problem is the jupyter notebook, its sometimes difficult to debug issues and takes a lot of time and is kind of vague as well in terms of application of the lectures.

Thanks to Andrew Ng and his amazing team, this course challenged my fundamental knowledge of Tensorflow and ML algorithms in the most useful way. However, I expected more applying libraries to design the NLP recent products.

some topics not explained in detail. Not enough examples to understand some models completely. As an example, I didn't fully understand what are the parameters for the models, their shapes, and how they are used in the model

Great course! But I find the transformer assignment bit difficult. I think if the implementation would be covered in a 10-15 minutes video it would be a lot more easier to understand. Thank you for the great specialisation!

Overall it was pretty informational on introducing NLP to me. However, Keras was a little bit frustrating to learn at the beginning. I found out the forum was a very good resource to learn Keras syntax whenever I was stuck.