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

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.

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.

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.

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

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.

Course videos are too short to convey the ideas behind the methodology. Illustration is too rough.

Another less than impressive effort in a specialization from which I expected more.

Explanations of attention/self-attention & other complex topics are too shallow

Course content more detailed explanation to follow.

elaborate and make it easy to learn

I would not understand much if I haven't known most of the material beforehand. Lots of repetition (not bad, just boring), but worse, bugs as well. Many times the lecturer doesn't know what he's talking about, and messes things up. Characteristic moment is when all of a sudden he talks about things without definition (like "shared frame", "adapter", or shows a diagram contradicting the code besides, etc.), or changes subject abruptly.

The grader is terrible crap happily returning errors but no explanation. You teach AI, you talk about LMs beating humans, yet the tool used for evaluating your students is so primitive as if written two decades ago. It's very likely that it infuriates everybody except its proud author. Either the code to fill is trivial (we learn nothing), or it requires mental work which potentially leaves some traces. The effect is that code works fine, but the grader fails miserably.

Like many of your courses, this one too teaches us more about the author's horizon and expectations, than new knowledge we pay for. This is particularly evident during quizzes where poorly formulated questions, answerable only in narrow context, abound. Also bugs like "translating french to english" require to mark "keys and values are the french words"...

The content is good because it covers many aspects of NLP. There are a lot of illustrations provided to help students understand the materials. However, the assignments are too easy because of the detailed comments provided. This makes it too easy because students could simply copy and paste the answers from the comments.

One suggestion is to improve the explanation of the materials because there re lots of details being skipped by the instructors. Personally, I would have to read other blogs in order to understand some of the details. Furthermore, separating the solutions from the codes is definitely something that must be done for instance presenting the solution in a separate notebook.

1) Information 3 out of 5:

no in depth explanations.

2) quiz are too easy, and I was missing good quizzes that were proposed at DL specialization with use cases, they cause me to think what to pick.

3) home tasks are 1 out of 5:

3.1 First of all all home tasks are done in different manner.

3.2 Some of them require additional check even all tests were passed.

3.3 Part with google collab is also a little bit strange... I want to have 1 click away home task and not setting up 3-rd party env.

What is good: for high - level overview this course is ok. Maybe have 2 versions of the course one with in depth explanations. and one more like this one.

Compared to Andrew Ng's deep learning specialization, this course requires a lot of improvement. Very often disparate facts are put together with not much connection between the ideas. This is probably because of the enormous amount of content covered. It might make sense to split the course into two. Thank you!

The course lectures were very confusing, and the course assignments were too easy, so they didn't reinforce the lecture concepts in the same way that assignments from other courses had.

Like: State-of-the-art NLP problems to be used in the industry.

Dislike: Topics were not well-explained. Difficult to grasp

Trax is no longer in active development.

Unclear explanation everywhere. Homework assignment cannot be graded

Too easy. I can say I almost didn't learn anything.