Tons of editing errors in lectures, and the programming problems rely more on knowledge of Keras (essentially untaught throughout the course) than they do on understanding of lecture material. A disgraceful end to an otherwise solid course sequence.

I really like the deeplearning.ai specialization. And also I like the Sequence Models course. However, I feel that I have learned less during this course comparing to the other ones in the specialization. First, I believe it was an extensive use of Keras. Whereas the framework is great, it would be much better for understanding if all the exercises were in numpy, whereas Keras tween-projects be optional. Doing both numpy and Keras versions would allow to better understand the material and learn through repetition. Second, even though the course is great, I perceived the number of errors/typos was much higher than in other courses. Is that true? For example, the Jazz Improvisation exercise was a nightmare. Overall, thank you for the course. Despite those problems, I would still recommend it.

Full of appalling errors that have been present for over 1 year. No one fixes it. It is clear that since Ng was let go by Stanford and Baidu, he is trying to earn a living with deep learning_ai. This apparently is not working as the small income from Coursera is not sufficient. As a result the prerecorded classes remain on Coursera to accrue some residual income. But, Andrew Ng and the staff are apparently gone.

Sadly, since these classes are no longer based on REAL Stanford classes the quality has gone downhill. I would recommend not taking the deeplearng_ai classes. Stick to classes offered by currently employed professors at established universities--preferably classes that ARE the same as the university classes or, at least, those derived from actual classes.

If not Internet, I would not have been able to study a world-class Deep Learning course at an affordable price. Thanks Andrew and team.

Loved the course - it was very interesting. It is also pretty complex, so will probably go through it again to review the concepts and how the models work. Thank you for this wonderful course series!

Keras is required to pass the assignments but no training provided for it. I can learn it myself of course but then the question is this - what am I paying for?

I want to thank Andrew Ng and his team for the amazing work. You definitely make the world a better place sharing this knowledge, and it is an inspiration.

To the contents: the course covers many uses of sequence models, for many different formats (many-to-one, many-to-many...), the questionnaires are focused but comprehensive and the programming exercises cover a wide range of difficulty levels, from no-brainer-one-liners (most of them) to implementing LSTM backprop by hand (optional). They take away the dirty work from you but make sure you get how you would do it. At the end you get to work with pretty complex setups like the attention model, but you still get the feeling of knowing how it ticks from the very bottom up.

The actual merit is that, even if it feels simple, it actually does work and is a takeaway knowledge that can be directly applied for personal setups. And mr Ng's videos are a charm, you can totally feel the care. Glad to see him back after so many years :)

Cheers

The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.

I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise

5 stars for the very informative lectures. I especially appreciated the amount of content related to de-biasing models. Such an important step to take!

2 stars for the HW assignments, however. I felt like I was just translating comments into code and that I didn't really learn enough to do sequence model development on my own without the hand-holding.

This course is by far the weakest out of the 5 course sequence. I did well in it (96.8%) but I think the programming exercises did not help build understanding of sequence models. Often I found myself just trying to get through the programming because I felt it was more an exercise in reading Keras documentation. I think you can pass this course without a solid understanding of what is going on in the sequence models. The programming exercises should be revamped to focus more on understanding what is happening in the program rather than trying to figure out Keras syntax (which is also useful, but perhaps better suited for a prep course).

I'm blown away by how quickly this series of courses brought me from thinking a neural network was a magic box full of fairy dust, to being able to understand even the (al)most complex of network architectures and what makes them tick at every level at a glance. A lot of time has obviously gone into structuring this course; not an ounce of fat present and the format of developing intuition before diving into the nitty gritty and optional further learning resonates with me on so many levels. Thank you Andrew Ng and the team at deepmind.ai and coursera!

I think it is the best courses designed so far. Gives you exactly the appropriate amount of information needed to understand basics behind sequence models. A must do course for all the students who want to pursue a career in this field.

Professor Andrew is really knowledgeable. I learn a lot from his lecture videos.

Great module, I am lucky to have used this resources in learning sequence models, I can imagine running LSTM using one of the frameworks without ever implementing it myself, Andrew Ng/Deeplearning.ai is the best!

Wonderful end to this Deep Learning Specialization. The programming assignments cover up a variety of hot topics in the Deep Learning market. The videos are very well made and teach the content in depth. A special thanks to Prof. Andrew for yet another amazing course in this wonderful specialization!

The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.

Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.

Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.

Could have been more thorough like previous courses

Great insights, helpful notebooks, good explanations.

Little bit math heavy. It was sometimes hard to understand the intuition, e.g. RNN, LSTM, GRU

Some coding assignments were too hard to follow what was required.

This one went a little fast for me, can't say that I'm confident on the shapes of tensors going through RNNs and why

This course has many inconsistencies and errors in the homework. Seems like a rushed job.