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.

The course is really good, one star less because it requires keras understanding to complete assignments properly. Including a basic intro of keras will help

The time allocated to some of the assigments should be increased. The estimated time in many cases seems to assume that one is aware of Keras and Tensorflow

To the point ; sometimes it would be nice to explain the research papers more in depth, and link other courses to have more formal mathematical explanations

Give us one more week to learn RNN for time series in economics, finance, etc!

Programming Exercises need more hints and more training in simple Keras models

Well-made course, but unfortunately there are tons of mistakes in the programming assignments - in the comments, formulas, even in the prepared code pieces.

The best course in the Deep Learning Specialization. Really good and well explained. There are some problems and mistakes in the problem assignments though.

Somes basics, tiny bit of theory, a bit of keras and insights for practical tasks. Some strage errors in notebook exercises makes it 2x time longer though.

Great course. The only tiny flaw is that the introduction to Tensorflow and Keras was a bit shallow so that I struggled a bit with programming these parts.

Instructive course with useful concepts. However, there were many more mistakes in the notebooks compared to the previous 4 courses in the specialization.

Awesome introduction, but feels like Andrew is a little bit rushing since it is the last course in the series, I dont feel it is as clear as other courses

The CNN course was better in this series of courses. This course is also good, but more content could be provided. Still the best small course out there.