this one was quite fast, should cover transformer in details and also other NLP models like BERT, GPT, MORE QUIZES should to be added based on tranformer acrchitechture, GRU and LSTM should me made short as LSTM is dead

Every notebook was super well explained and made except the last one, which was very confusing, unfortunately. I did not enjoy doing that one :( Also, I think the transformers network were not deep explained as hoped.

Video content is excellent but I am not very much happy with the assignment task. There should must also be some video content based on the assignment because the some codes some libraries are not taught.

In comparison with other courses in this specialisation a lot of assignments were poor quality - vague descriptions and code logic (especially week1, asign 2 & 3) or just broken (last week3 assignment)

With respect to the others, this one seems to be prepared almost in a hurry and the learning curve is very steep and sometimes the programming assignment don't have a nice progression as the others.

Videos were really informative and were equally interesting, but I believe that the programming assignments lacked a bit in clarity. The instructions were really unclear, it could have been better

Quite a few bugs or abstractions in this course, in comparison to the others the projects feel a bit rushed and pushed together. Andrews's explanations and video lectures were still great though.

this course is the most difficult in deep learning specification, but i think Andrew NG should design more homework for word embeddings and bidirectional rnn, i do not understand how it works yet

The course was great. However, coming from finance I was also hoping to see some examples which use time series so I can get a picture of how I can extend this knowledge to my specific domain.

The module was really good in explaining the concepts, but there wasn't any deep dive on the equations and mathematics behind with the results of making the code assignment harder to achieve.

I gave 5 stars to other courses in this series but this one doesn't deserve 5 stars. There were many typos and bugs in the assignments compared to the other courses of the specialization.

Video Lectures were excellent.

Assignments, however, were buggy and spoonfed you too much. I completed them, but on more than one occasion, I had no idea what the code was actually doing.

The lectures are wonderful as usual but the practical assignments are so extraordinarily perplexing that it does not seem to improve the learner's comprehension by a significant extent.

Boring course:

Short videos instead of lections.

Copy-paste task instead of exercises.

Loading pretrained models instead of training.

Good full keras tutorial will be ten times more useful.

The lectures were not as good as the previous andrew ng. courses, and the exercises were quite bad in all honesty.

I do appreciate what I have learned, as the lectures WERE clear enough.

Just one observation, the general view I have from this course is very positive but compared to the other courses the program exercises in this course are more difficult to understand.

A good introduction in Sequence models. Unluckily the programming assignments are more of a guess work then real knowledge as there are too few possibilities to check the own code.

Despite being a great introduction to many NLP concepts, the programming exercises are a bit too much "fill-in-the-blanks" and not challenging enough. It is still a great course !

Course contents were very good, but the assignments could have been improved a lot. Couldn't get much value out of the assignments rather than blindly following the instructions.

The class covered too less on the Sequence models such as LSTM and GRU. It has too much domain knowledge related videos. Also, it does not have any video related to Time Series.

The videos are good, however the assignments are too long with sooooo many text inside. You have to make it more efficient somehow.

But thank you anyway for the great content!

Assignments in this course are relatively less organized. You have to read a lot of description first and then code and have to go back/forth between code and description.

I believe that the course needs more time allocated to the incremental teaching of this rather large subject area with varied applications. Just needs to be a better way.

The course was especially interesting in week 2 and 3, but the assignments for week 1 were confusing and sometimes it was hard to follow where the logic was coming from.

This was the most difficult to understand course in the whole specialization. Would have enjoyed more if the course material was a little more spaced and elaborated on.