Great course overall with lots of technical details. Week 4 content on transformers is high-level and makes it difficult and time-consuming to complete the hands-on exercise.

Excellent content, although the exercises - at this stage of specialization - should focus more on NN architecture, than on pure computational and matrix slicing tasks, imho.

Even though it has some flaws in audio clips and homeworks, programming assignments are fun to do. Great real world applications on this whole specialization is just perfect!

The content was quite good as with previous courses in this specialization. Just dropping a star because week 1 material wasn't as clear to follow as the rest of the content.

Compared to the first two sections, I don't think this section is better than those. Anyway, I learnt some concepts about sequence models which I need to dive into in future

A great course on NLP including Transformers. One star down for quite a brief introduction to transformers (I understand that time passes but you better update the course)

The videos are of high quality as always, while the programming exercises had some error in it. Compare with the previous few courses this one seems not polished so well.

I learned a lot, but it really felt like there could have been a whole other week's worth of material, especially talking about newer innovations like transformers, etc.

This course and assignments helped me understand concepts in NLP. However, it is a little less comprehensive compared to the previous courses in the deep learning series

Thank you Deep learning team for putting together this course. The course has really helped me understand the various possibilities with the knowledge of deep learning.

The first week was a bit too tough compared to the second and third. So, I felt it was a bit hurried. It could have been distributed into two separate weeks, perhaps.

This course was very useful. I would make the programming assignments for the first session a bit clear. Other than that, everything was easy to understand and clear.

Good, but not as in depth as the other lecture series I found. It is faster paced and skips over much more of the detail at which they go into in the earlier modules.

If the programming exercises weren't plagued by some bugs, I would have given 5 star. It's a very complex subject matter, but Andrew takes you through it by the hand.

I feel week 2 and week 3 materials were covered a bit too quick. Would appreciate more explanation of the implementation details of beam search and activation model.

This last course (and especially the Week 4 material about transformers) feels much more rushed and less detailed than the previous courses in that specialization.

It was really helpful, every topics explained very well. However, in my standpoint of view, It did not cover some part in sequence learning, like graphical models.

Really nice course. Very informative. Unfortunately some programming exercises were a little buggy (the grader especially)...only a total reset of notebook helped!

Great course, I learned a lot. The notebooks in courses 4 and 5 are a bit too long, they take a considerable amount of time, much more than the videos and quizes.

Wonderful, I just felt at times that the exercises were more similar to general programming than sequence modelling. Still, 10/10 course, would totally recommend.

this course provide an adequate and what you want to know about recurrent neural network but it does require lots of programming skills to accomplish this course.

Good course, but I needed more time than expected, especially for the exercises. For me, that was the most demanding course out of the 5 from that specialization.

A lot of cool material covered from RNNs to LSTMs to Sequence Modeling. But it is a lot to grasp and a lot to understand. Overall, rigor and course is decent.

programming assignments are not teaching us to code independently because of the helpers functions given in utils file. Feels like copy pasting the assignments

It is a really awesome course for those who want to get started with deep learning methods in NLP.

Got a very clear insight about GRU,LSTM,RNN,Word Embeddings.