Excellent course to get the nuts and bolts of sequence models and natural language processing using deep learning.As always instructors have been absolutely brilliant.Really really enjoyed the course.The course offers excellent programming assignments.

Such an amazing course. Andrew is such a great instructor. Actually, it is thanks to the whole Specialization that I'm making now my own career as Chief AI Scientist for a Health Tech Start Up.

I'm endlessly grateful to Andrew and this Specialization!

This course, as always, de-mystifies a quite complicated topic and gives very good intuition why the models work. For example, LSTM is very complicated unit but Andrew illustrates well what it tries to achieve. That said, this is a very tough course.

This course covers a lot of ground and provides many opportunities to practice the concepts. I just wish there were one or two (possibly optional/honors ) assignments with fewer walk through instructions so one could test one's learning for real.

This was very cool, RNNs seem very powerful. I don't think the programming exercises were the best to get some experience but I really like the way Andrew explains the algorithms. Still 5*

(They are totally using these reviews for text analysis :P)

This is a pretty cool course to work on introduction of NLP. A blend of practical and theoritical aspects included though there are black boxes (I felt Convolutional Course to be more effective though, maybe because of my background, but still!!)

Andrew Ng and the team have created a fantastic opportunity to learn the basics of Deep Learning Algorithms. "Hearing from Lion's mouth" was a great experience for me. I would strongly recommend any deep learning enthusiast to attend the course.

somewhere between 4 and 5 stars. It's a good course to make you understand about major knowledge to process sequence by DL, and the assignments are the hidden gems.

Some recent results are not included (of course), such as BERT. But it's till OK.

Great course teaching state-of-the art NLP technologies. I wish the attention notebook could be improved, and the projects could get more flexible and in-depth. I also wish there could be a Keras tutorial that gives an overview of the framework.

Andrew Ng is a fantastic instructor. His ability to break down a complicated concept like neural networks and deep learning so that a non-math, non computer person like me actually understands the underlying mechanisms is quite extraordinary.

I think the course and specialization are special. It requires a lot of hours to finish it. It can be a great review of specific topics in the deep learning subject. Thank you for allowing us to reach those specific topics easily. Great content

The course explains the fundamentals of RNNs, GRUs, and LSTMs in a very easy manner with plenty of illustrations and programming exercises. Although, some of the exercises are optional but doing those actually help reinforce essential concepts.

Très intéressant. Les papiers scientifiques illustrés dans les travaux pratiques sont récents et cela fait plaisir de comprendre comment marchent ces réseaux de neurones de notre quotidien (traduction google, trigger word, style transfert, ...)

State-of-the-art and in-depth content, inspiring and engaging lectures throughout the whole specialisation. I will be eternally grateful to all of you who created this (and the other) courses. Thank you, Coursera, thank you so much, Andrew Ng!

Very useful for understanding what 'sequential' means in DL. I expected as some new solution to the time series scenarios but it turns out a different area from the quantative techniques. It might be helpful applied to more 'sequential' areas.

Yes, I have just finished the whole specialization. I'm very proud of myself. And I recommend this specialization to anyone who wants to start with this field. Andrew is the best one I've seen who able to explain the thing in deeper way. Thanx

A wonderful, intellectual experience!

Don't fret if you are having to look up a few documentations, the comprehensive documentations of the built-in functions/layers needed are accessible on the official websites of the libraries(Keras, NumPy).

"I think that every person whether you come from a wealthy family, or a low income family, or anywhere in between. I think everyone should have great opportunities. " (socioeconomic status)

I was shocked by this speech. Thanks to Prof. andrew

The course was brilliant! Even though, the programming assignments could be better (personal opinion). The teaching itself is brilliant, and I think Andrew NG is by far one of the best Deep Learning teachers for anyone looking to get started.

very nice course but bit comprehensive especially Sequence models.

Expecting a same comprehensive course in Python development also so we can not write just a piece of code but entire code.

Also expecting a course on Keras , and end to end one.

More lectures are needed in the sequence models to make these models or ideas clear to deep learning learners. I found this course is the most difficult one to follow in this series even if I got quite a lot experience on time series topics.

It was a great learning experience. Thanks Andrew Ng for the wonderful content. However, there are still some mistakes in the programming assignment such as in the expected output of 3rd assignment. Kindly correct it. Overall very satisfying!

It's an amazing course, the video lectures are fruitful and the contents of the courses are well designed, the instructor is talented and his explanations are extremely helpful, it's one of the best courses taught on keras!

Thank you so much!

Great course! I feel some great accomplishment having finished this specialization I began 2 months ago. This one course could use a bit more of Keras hints or optional Keras course for those who do not have a regular use of the framework.

This course is great if you want to know about sequence models for areas like nlp and speech recognition. They provide assignments that is solvable and their forum is active, and you can always find people solved your confuse on assignments.