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.

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.

The lectures were great. Andrew is a wonderful teacher, but the assignments were beyond miserable. Jupyter notebook is probably the least stable, most infuriating piece of software that I've been forced to interact with. I spent countless hours trapped, not able to perform the most basic of operations, such as saving my work or submitting. I lost work innumerable times. I, like others, eventually resorted to "saving" my work by copying it into a text editor on my system for fear of Jupyter sabotaging me. Even if the system was stable, most of the assignments were worthless as learning experiences. The majority of "programming" boiled down to playing a cryptic game of fill in the blank. Bottom line, I wouldn't recommend this class to anyone in its current state. Especially as a paid service. I really expected better quality.

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.

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).

To set the context, I have a PhD in Computer Engineering from the University of Texas at Austin. I am a working professional (13+ years), but just getting into the field of ML and AI. Apologies for flashing this preamble for every course that I review on coursera.

This course is the 5th and final one in a 5 part series offered by Dr. Andrew Ng on deep learning on coursera. I believe it is useful to take this course in order and it makes sense to study it as a part of the series, though technically that is not necessary.

This is one of the best courses to take if you want to understand the basics of Sequence Models (Recurrent Neural Networks). RNN is a technically-difficult-to-understand, still-evolving field of Neural Networks, and it has thus far found remarkable uses in a wide variety of field, ranging from Natural Language Processing (NLP) to Voice-to-Text conversion and Music Synthsis, to name a few. Dr. Ng really exposes us to this cutting edge research, by explaining research papers that were only recently published. By now, I could see how the problems would be tackled. However, there are several subtle aspects, such as the optimal metrics to use, the clever modification in the NN architecture, etc. which Dr. Ng drew attention to and made clear.

The instructor videos are very good, usually 10 min long, and Dr. Ng tries hard to provide intuition using analogies and real-life examples. The quizzes that accompany the lectures are quite challenging and help ensure that the student has understood the material well. As with the other courses, the programming exercises are the best part of the course. You get to practice, (1) Music synthesis, (2) NLP and Sentiment Analysis, (3) Trigger Word Detection (Hello Google, Hey Siri, Alexa!), ... All these problems are actual, real-life projects, which are extremely difficult to solve. They help the student practice the strategies and also provide a jump-start for the student to use the code for their own problems at work or in school.

Overall, this is an excellent course. Thank you Dr Ng and the teaching assistants, Thank you coursera.

I HAVE A HUGE GRIPE WITH COURSERA's TECHNICAL SUPPORT. THEY DO NOT HAVE A READILY AVAILABLE TECH-SUPPORT EMAIL ID. YOU HAVE TO SEARCH THROUGH THE WEBSITE CAREFULLY TO FIND A CHAT LINK. I HAVE RECOMMENDED COURSERA TO SEVERAL FRIENDS AND MANY OF THEM ARE VERY UPSET AT THE SUBSCRIPTION POLICY, WHICH IS SNEAKY. IN FACT THE WORDING IS ALMOST DESIGNED TO CHEAT YOU.

I have been a huge supporter of Coursera and hate to give this negative feedback here. I would have easily subscribed to many other useful and important coursers that Coursera offers, but will now be doubly careful about doing so.

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

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

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?

Videos are okay, but exercise is just debuging!

Professor Ng seems a little bit confused about the subject and is making unnecessary analogies rather than going deep into the algorithm and explaining the context as he did in Convolution Neural Networks course. I hope that the videos are revised and professor explains the topic more clearly rather than depicting himself to be confused as well on the topic.

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.

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!

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.

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!

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.

Learnt a lot about new concepts in RNN and LSTM. Really wanted to learn about these models. This course helped a lot. Everything was new and so fascinating. Loved this course and our teach Andrew NG.

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.

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.

Unfortunately considerably a subpar course compared to the other four in the specialization. Programming assignment is a mess - wrong formulas presented, nowhere near enough Keras's tutorials, etc. Every assignment is passed by browsing the forum looking for help from other people. It is unclear to the point of being annoyed (got someone in the forum cancel his subscription). However, lectures are fine and sequence models cover a wide range of areas/applications, so you can't miss it anyway.

Assignments don't match the quality of the other four courses of this specialization. Automatic grading accepts solutions despite results not matching expected results. This should be fixed.

Many topics are only quickly skimmed over. Serves as an overly brief introduction to RNN.

super unorganized!

really really bad

Very good. I have no complaints. I though instruction was very clear. Assignments were very helpful and challenging enough that I learned something, but not so challenging that I got stuck too often.

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.