C​ontent wise it's excellent as always, I am not giving 5 stars, because of two reasons: a) audio including transcript is sometimes not of best quality (in english) as it would be generated by a machine b) readings are very brief and just quickly summarizing what has been taught in the video (could go in more depth). I would give 4,5 stars if that would be possible.

The course gives an encompassing overview of the latest tools and technologies which are driving the NLP domain. Thus, the focus gradually shifts from implementation and towards design.

Since the models require specialized equipment, they go beyond the scope of a personal computer and create a requirement for high-performance computing.

Great content, although the focus is definitely more on the attention mechanisms and on the Transformer architecture than on the applications themselves. Still really enjoyed it and I now feel like I have a better grasp of Transfer Learning and its associated methods. Content is very clear and well explained

This is really an interesting specialization with lots of things to learn in the domain of NLP ranging from basic to advanced concepts. It covers the state of the art Transformer architecture in great detail. The only thing with which I felt uncomfortable is the use of Trax Library in assignments.

Covers the state of the art in NLP! We get an overview and a basic understanding of designing and using attention models. Each week deserves to be a course in itself - could have actually designed a specialization on the attention based models so that we get to learn and understand better.

It would have been better if we use standard frameworks like PyTorch instead of Trax. Also, the Course Videos are a bit confusing at times. It would have been great if the Math part would have been taught as Andrew Ng Taught in Deep Learning Course.

Not new to NLP, I enjoyed this course and learned things I didn't know before. From an educational perspective, I didn't like that the two "optional" exercises were way harder than the too easy "fill in x here" assignment.

Overall good quality, but seems a bit short and content are squeezed.

I don't like the push of Trax neither, it is has yet become the mainstream and personally I don't find that helpful for my professional career.

I think that we could go deeper in the last course because you taught a lot of complex concepts but I did not feel confidence to replicate them. It was better to explain transformers with more detail

It's a great course, more difficult than I thought but very well structured and explained. Although more didactic free videos can complete the lessons from others websites.

great course content but go for this only if you have done previous courses and have some background knowledge otherwise you won't be able to relate

good course covers everything i guess, the only down side for me is trax portion, i would've prefered if it was on TF maybe, but still great job

The course covers cutting edge content and the exercises are well paced. Found the transformer lessons a bit difficult to understand.

Not up to expectations. Needs more explanation on some topics. Some were difficult to understand, examples might have helped!!

need more detailed explanation in the last course of this specialization, especially Attention and BERT models.

I​t's a really good course to learn and get introduced on the attention models in NLP.

An amazing experience throughout the state-of-art NLP models

Labs required a bit more context, to understand.

The programming assignment is too simple

great explanation of the topic I guess!

Awesome course

Though the content is extremely good and cutting edge, the course presentation/instructor hasn't been able to do justice to the course. [1] Teaching concepts through assignments (and not covering them in detail in lectures is) an absolutely bad idea. [2] Lecture instructions are ambiguous and immature at times. Instructor is an excellent engineer but a bad teacher is very evident from the way of presentation. [4] Only if input output dimensions were mentioned at every boundary in network illustrations, would have made a lot of difference in terms of speed of understanding without having to hunt through off-line material and papers. [5] Using Trax I think is not a good idea for this course. The documentation is kind of non-existent and lot of details of functions are hidden and the only way to understand them is to look at the code. A more established framework like Tensorflow or pytorch would have been much more helpful.

Overall a disappointment given the quality of other courses available from Coursera.

Pros: Good choice of content coverage. Provides a historic overview of the field, covering the transition from early work on seq2seq with LSTMs, through the early forays into Attention, to the more modern models first introduced in Veswani et al. Week 4 covers the Reformer model which was quite exciting. Decent labs

Cons: Videos aren't great, there are a lot of better resources out there, many actually included in the course's reference section. Trax is not a good framework for learners in comparison to Pytorch, but if you plan on using TPUs and appreciate the pure functional style and stack semantics then it's worthwhile. The labs can be a bit copy-pasty. Some of the diagrams are awful - find other resources if this is a problem.

Overall: I'd probably rate this course a 3.5 but wouldn't round up. The videos really let things down for me, but I persisted because the lesson plan and labs were pretty good.

Even though the theory is very interesting, and well explained the videos dive too deep in certain concepts without explaining the practical things you can do with them too very well.

The practical stuff, especially the graded assignments, are very centered around Trax, and the only things you have to know and understand are basic python and logic. You don't really get to make your own stuff, you just fill in stuff like "temperature=temperature" or "counter +=1".

I preferred and recommend the first two courses in this NLP-specialization.

The labs in the last two courses were Excellent. However the lecture videos were not very useful to learn the material. I think the course material deserves a v2 set of videos with more in depth intuitions and explanations, and details on attention and the many variants, etc. There is no need to oversimplify the video lectures, it should feel as similar level as the labs (assignments tend to be "too easy" but I understand why that is needed). Thanks for the courses. Azriel Goldschmidt