This course has great material on sequence models, presented with the usual energy and enthusiasm that Andrew Ng brings to every course. The model diagrams are great for visualizing what's actually going on in the complex assignments, and the assignments are generally designed with 1) additional code and commentary to make the examples informative and 2) the "guard rails" (e.g., insert code here, with related hints) to clarify the specific lines to edit. Having said that, I feel that the assignments were a little less polished/refined relative to earlier courses in the concentration, and I spent significantly more time in the discussion forums than I had in prior courses. I also recommend investing in additional training in Keras and Tensor Flow as a prerequisite or in parallel to this course to help get the most out of the practical applications of the material.

As i progressed through the specialization i liked the content less for the following 3 reasons. 1) With the exception of the first course there are no in-video questions which would help learn/exercise on the spot. There could easily have been one question per 3-4 minutes of video content. 2) The programming exercises although useful, became more disconnected from the lectures as the specialization progressed- ie they focused on keras documentation which could have been accompanied by a lecture video dedicated to that goal (as in the second course with tensor flow). The result was that I just tried to get through the programming exercises in courses 4 and especially 5 without understanding much of what I was doing. 3) There were too many errors in the lecture. Overall a good specialization but at least points 1 and 2 should be fixed

this was a really interesting course. Too bad the details of the math are not really there.

That being said, its greatest redeeming factor is that Andrew cites the research papers, and with his overviews of the various models, I can read those papers, and build up my own library of relevant material.

I don't really care for Jupyter Notebooks after all...I much prefer the Spyder IDE, as it has intellisense, true debugging, and is not prone to crashing the kernel.

I came across some of Andrew's course lecture notes for his CS courses at Stanford - now those have much more mathematical detail - perhaps Coursera can provide the links to the online material? ( unless, of course, that is problematic due to copyrights?)

All in all, I did enjoy the entire deeplearning.ai material as it is....the rest I can dig into myself.

It is a good course and a very important one. However, I needed to mark it a bit lower than most other courses in this specialization because I felt sometimes confused with Keras. Navigating in such higher level of abstraction would require a stronger support for the Keras part. I believe we could have explored a bit further the sequence models and yet I was sometimes struggling understanding some basic Keras instructions. Perhaps it could be included an extra programming assignment tutorial (for Keras) or extend the existing Keras tutorial.

Being this the last course of the specialization, I believe not only this course is worthwhile, but the whole specialization is of great value. Congrats to all Deeplearning.ai team. Keep going.

First of all I would like to convey my thanks to Andrew Sir for not only this course but for the whole specialization.You are fantastic teacher and I will try to pay you back by solving real world problems with the help of knowledge you have imparted.

The only short comming I can think of is the disconnection between your theory videos and the real codes in python.It would be very helpful if you can include some code snippets in your theory videos.I think this will make the learners better bridge the gap between the theoritical concepts and real life coding. Maybe some optional hands on coding videos summarizing the week's course can be included.

Once again thank you very much and I would be ever grateful to you.

nice course as always! I need really thanks Andrew and team for this, it is very well structured & informative, provide good intuition and solid base for future self-learning of this area.

However to get full 5 for this course, there are some thing to improve ( video cuts ~ repeatable sections, sometimes mistakes, long pauses ) , also courses some of them was harder to pass trough aka from descriptions and template was not certain what to do ( one thing it is good that you need to think more and reread x-times, however sometimes grader vs 'official' output are not aligned which results in wasted time ~ hours ) ~I guess most of it was because it was rushed out too soon, but evendo very good one!

Overall, I liked the Machine Learning Stanford class' programing assignments better than the one in the deep learning specialization. For me, coming up with a full implementation of a function (and then having it unit tested by the grader), is more conducive of learning and more entertaining than a step by step, line by line guidance, as we get in the Jupiter notebooks. That said the notebook themselves are incredibly well designed and put together. I love how Andrew Ng, beyond his stature, unmatched knowledge, and outstanding teaching skills, puts his whole heart to work. That makes the world of a difference to me and helps me do the same with others. Thank you for everything!

This was a VERY thorough overview of the machine learning architectures required to tackle a wide range of natural language processing problems. It's quite dense and I had to watch each lecture several times and break it down into chunks to avoid getting lost, but now that I'm finished there I feel like a lot of technology has been demystified. The assignments really hold your hand and mostly just test your ability to follow instructions with even a hazy understanding of the weekly concepts, so you shouldn't expect to graduate and then immediately build a machine translation system from the ground up, but I do feel very ready to dive into technical interviews.

This was a good course mainly on Recurrent Neaural Networks, including LSTMs, and Encoder/Decoder networks, and the Attention model. In the last week there a brief overview of the Transformer networks and a long exercise fleshing that out. This is an interesting combination of merging Recurrent Network and a convolution style network.

The one think that has me leaving off the last star is that there were no lecture style classes training users how to use tensorflow, or how tensorflow works. That was all learned 'on the programming exercises', so it is helpful to have deep experience with Python, and with using different libraries and styles of coding.

Review of the 5 courses:

Good:

Well summarized lectures that are easy to understand. Everything is broken down into small problems making most of the content accesible.

Interesting programming assignments, which are well structured.

Bad:

Jupyter notebooks, where the programming assigments are done crash often.

On rare moments I did require extra material from youtube or medium to understand what was going on.

On the quizes, formulas are not correctly visualized and I can still see the markdown code, making it hard to read the formulas correctly.

Some technical issues in the course but I would highly recommend overall.

A very helpful and enlightening course, though it felt a bit "hand-wavy" at times. It never really felt like we were getting the full story, like I was missing something the whole time. Word embeddings cleared up a lot, but the entire course was a lot of information to digest at once. Coming from an image processing background, most of the terminology was unfamiliar, and the programming assignments weren't quite as guided as previous ones.

In the end, I think it was a great course, and I'd recommend it highly to anyone interested in the field. If you can't apply it to your work, it probably isn't as beneficial.

It's a lot of great content for becoming aware of all the various concepts in deep learning. And it does a great job of pointing at the original papers explaining all of those things. The homeworks are often very pointless: a lot of learning exactly what things they want you to copy paste, but minimal understanding of the actual algorithms, especially for the later more complicated ones. (Transformers remain a complete mystery to me, this course did not explain them sufficiently or connect them to CNN or anything else clearly, and the homework was especially meaningless in this regard.)

I'm a fan of Andrew Ng's machine learning classes on Coursera. This was my least favorite. I'm not sure if it's because of the complexity of the material or that so much material is presented in a short time, but I feel that I'm not as confident about my knowledge of the material in this course compared to the earlier courses. In the last few assignments, I felt like I was mechanically plugging stuff in without really understanding the thought process. His teaching style seems much the same as the other courses though, so it's possible this could be due to me rather than the course.

I am grateful for the opportunity to have learned from an exceptional instructor, and one of the luminaries, in artificial intelligence. Insofar as this particular course is concerned, theory was well explained, as always. I feel like there was a bit of a disconnect in the implementations, though. Some of this was just the sheer challenge of using a still-unfamiliar platform (Keras). And, in concert with this latter point, some was due to a sort of "fill in the blank" approach to using the platform. Nonetheless, that I have learned, and learned a lot, is undeniable!

the last exercise is a bit hard to understand especially

the Exercise 4 - EncoderLayerhttps://ntwjrryqcvtz.labs.coursera.org/notebooks/W4A1/C5_W4_A1_Transformer_Subclass_v1.ipynb#Exercise-4---EncoderLayer

which says You will pass the Q, V, K matrices and a boolean mask to a multi-head attention layer. Remember that to compute self-attention Q, V and K should be the same. Let the default values for return_attention_scores and training. You will also perform Dropout in this multi-head attention layer during training. 

altough Q,V,K does not come in as function inputs.

a more intuitive materials been used while teaching would be helpful to more effieciently and enjoyably grasp the concepts. what i mean is that the description or the summary the lessons been taught in a week are in the corresponding week's assignments; those summarys were more clear and visually pleasing than the inclass presentation. for example, usage of pens for drawing networks and the likes can be migrated to better animations ,etc. the crux is that the content in the course is great, but it feels like there is a good scope for improvement in presentation.

Firstly, thank to the course instructors and Dr.Ng for teaching us deep learning. You are all a gem. I enjoyed this course, and how simple it made coding RNNs. However, I believe the concepts could be simplified some more, even in the form of a pseudocode or conceptual outline. This is my 3rd course from Andrew Ng, so I know he's skilled at distilling deep learning concepts with ease. Week 1 was the best for me as the operation of the LSTM, GRU RNNs were succinctly outlined and set a solid foundation , Week 2 could be presented a less abstract way though.

I think with sequence models, the course details were very challenging. I strongly believe that do take a course in Deeplearning Specialization, one must at least learn Python from basics to advanced level. However, Andrew Ng has made it easy for a first time student with programming background to understand most of the concepts in this specialization. Thank you Deeplearning.ai for this course. I have learned some of the cutting-edge skills that can't be easily found anywhere. I have learned a skill that will set me apart from the crowd.

I really enjoyed this course. I'm not crazy about the fill-in-the-blank style of the programming assignments. I think I'd learn the material better if it just gave me the arguments and returns of the functions and forced me to write everything in between. I think it makes sense to emphasize keras in the later parts of this sequence, but I feel like I never got a basic introduction to how models in keras are supposed to be structured. Maybe there should be an assigned reading on this, if not a video or an optional programming assignment.

Great content, and leaves me set to build systems making predictions for or conversions between sequences- particularly including text posts, which are an interest of mine.

Deducted a star because a couple of ungraded exercises contained errors which had been left uncorrected; they were still valuable, especially the manual implementation of backprop one, but there's some missing attention to detail there. But the level and effectiveness and practical applicability of the course remains excellent and I'd still heavily recommend it.

I found the first week of this course a bit tough compared to all the other 4 courses in this specialization. Perhaps there should be one more week to give much more programming exerises to help understand the concepts clearly. But having said that, the last two weeks, especially the last one about hot-word, is very neatly done and provides very good understanding of such models are implemented. Overall satisfied. Thanks Andrew and team, I feel much more confident in my understanding of these terms and the concepts behind them.

I never been exposed to this subject Sequence Models before. I learned a lot from this course. But the materials is more advanced than all previous ones, especially the program exercises. The exercise guideline is helpful but not leave many guess works for students not well skilled in Python and Keras. I completed the program exercises by blindly trying different keras commands.

Little suggestion: include a short but complete example code for building Keras Sequence models in the tutorial.

Over all, a great course. Thanks a lot.

I can't quite give this a 5. The lectures covering transformers did not show the same amount of effort or clarity that I know Andrew Ng is capable of. And the transformers-related homework was challenging could have provided a little more background. (And the unit-test error messages were not as helpful as in earlier assignments.) Honestly, the topic of transformers felt a bit rushed. But every other topic discussed in this course was perfect (just like all the other courses in this deep learning specialization series).

Overall, it is very good course unless for some minor problems with the assignments.

For example, in Week1 the optional assignment, there are many bugs there, one may waste a lot of time trying to figure out the correct solutions. Though, it has been widely discussed in the forum, the instructors should have updated the material or at least warn the students somewhere in the assignment to read forum ahead of time. You must admit that many won't resort to the forum only after trying and wasting enough time..

Hope may help.

Much more challenging than the other courses in the DL specialisation. It forced me to delve a little deeper into the topic in order to overcome obstacles in the assignments. Content-wise, it's a great introduction to DL for NLP. Professor Ng's explanations are perfect.

Admittedly, compared to the other courses, this one is "messier". Spelling mistakes, some contradictory instructions, and a somewhat broken notebook for the last assignment. It felt rushed and I'm surprised that a lot of the errors haven't been fixed yet.