Can the instructors make maybe a video explaining the ungraded lab? That will be useful. Other students find it difficult to understand both LSH attention layer ungraded lab. Thanks
This course is briliant which talks about SOTA models such as Transformer, BERT. It would be better to have a Capstone Project. And entire projects can be downloaded easily.
创建者 RAHUL J•
Not up to expectations. Needs more explanation on some topics. Some were difficult to understand, examples might have helped!!
创建者 Vaseekaran V•
It's a really good course to learn and get introduced on the attention models in NLP.
创建者 David M•
An amazing experience throughout the state-of-art NLP models
创建者 Shaojuan L•
The programming assignment is too simple
创建者 Fatih T•
great explanation of the topic I guess!
创建者 Sreang R•
创建者 Amit J•
Though the content is extremely good and cutting edge, the course presentation/instructor hasn't been able to do justice to the course.  Teaching concepts through assignments (and not covering them in detail in lectures is) an absolutely bad idea.  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.  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.  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.
创建者 Laurence G•
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.
创建者 Christine D•
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.
创建者 Rishabh S•
The course is very research oriented and not very useful for data science practitioners. No time was spent on explaining how transformers can be used for NLP tasks using a small domain or company specific corpus through transfer learning. I'm not planning to develop the next blockbuster NN architecture for NLP and so the intricate details of how transformer and reformer works seemed like an overkill. Lastly, using Trax instead of the more production ready frameworks like Tensorflow also made it feel very research focussed.
创建者 Azriel G•
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
创建者 Thomas H•
While the course succeeds in getting the most important points across, the quality of both the video lectures and the assignments is rather disappointing. The more detailed intricacies of attention and transformer models are explained poorly without providing any intuition on why these models are structured the way they are. Especially the lectures on current state-of-the-art models like BERT, GPT and T5 were all over the place and didn't explain these models well at all.
创建者 Kota M•
This course perhaps gives a good overview of the BERT and several other extensions such as T5 and Reformer. I could learn the conceptual framework of the algorithms and understood what we can do with them. However, I think the instructors chose an undesirable mix of rigour and intuition. The lectures are mostly about intuition. In contrast, the assignments are very detailed and go through each logical step one by one.
创建者 Zhuo Q L•
It is exciting to learn about the state of the art approach for NLP, but as the last course of the specialization, one can feel that the quality/level of details of descriptions just dropped significantly. I like how the course introduces useful things like SentencePiece, BPE, and interesting applications, but some of them felt abrupt and wasn't elaborated.
创建者 Dan H•
Pros: Good selection of state of the art models (as of 2020). Also great lab exercises.
Cons: The video lectures and readings are not very helpful. Explanations about the more tricky parts of the models and training processes are vague and ambiguous (and some times kind of wrong?). You can find more detailed and easier to understand lectures on Youtube.
创建者 dmin d•
Have to say, the instructor didn't explain the concept well. A lot of explanation doesn't make sense, or just give the final logic and skip all the details. I need to search on youtube or google to understand the details and concept.
But, it covers state-of-art models for NLP. It's a good starting point and helped save time.
创建者 Oleksandr P•
Although this course gives you understanding about the cutting edge NLP models it lacks details. It is hard to understand a structure of the complex NLP model during the few minute video. This course should have step by step explanations in the bigger number of lectures or increase their duration.
创建者 Nunzio V•
Nice course. Full of very interesting infomation. What a pity not having used Tensorflow. All that knowledge is unfortunately not work-ready as Trax is not widespreadly used in the industry world and it is hardlyit will ever be. In my opinion.
创建者 Семин А С•
Explanation of Attention models with Attention mechanism itself and other building blocks of the Transformers was very confusing. It was really hard sometime to udnerstand what the lecturer really meant.
创建者 Michel M•
The presented concepts are quite complex - I would prefer less details as most will not understand them anyway and more conceptual information why these models are build as they are
创建者 Zeev k•
not clear enough. the exersices warent good enough' i didn't learned from them much. it could be a great idea to give the slides at the end of every week for reapet.
创建者 Huang J•
Course videos are too short to convey the ideas behind the methodology. Illustration is too rough.
创建者 Maury S•
Another less than impressive effort in a specialization from which I expected more.
创建者 martin k•
Low quality programming assignments, but considering the price it's good overall
创建者 Prithviraj J•
Explanations of attention/self-attention & other complex topics are too shallow