The theory was absolutely interesting and an eye-opener. The programming exercises were hard to make because of the keras/tensorflow knowledge and I actually ended up just fill-in in things, as were given in the examples, without really knowing what I was doing. Time for a Tensorflow course.

Poderia dar um pouco mais de instruções na hora de usar as funções do Keras. Ficou um pouco confuso.

No assignment da semana 3 (Machine translation). Tem um output que não precisa estar exatamente igual mas o curso não fala isso e acabei gastando bastante tempo nisso, só vi depois no fórum.

Though the lessons are interesting and mathematically demanding, I felt that it was a good time spent learning these concepts. Overall I feel that the 3 weeks could be split into 4 weeks and learning could have been much smoother by adding some more lessons to address the contents of Week 2.

I Love it! every single part of the course!

The reason I give it 4 stars is to get the attention of the developers and lecturers of the course.

Please, please, please improve the customer service of coursera. Please give the site deeplearning.ai more support from lecturers and people experts.

Nice content, but the assignments are too easy and only demonstrate the pipeline instead of providing hand-on experience in picking the network and training with GPU. Also, there are some grader problems which has wasted my time to make my code pass the grader even if the answer is correct.

The homework grading methods need improvement. I got the right model and get the right results, but still have to spend tons of time to make the submission pass the grading system. It is a waste of time for future learning. You may want to train a DL model to solve this grading problem :).

Great explanation of theory (RNNs etc.) and easy to follow course structure. The programming exercises are disappointing though: They mostly consist of mindlessly copying Keras functions without an understandable (!) explanation. Many provided links to the Keras documentation are outdated.

Assignment for week 4 offered little instruction. The exercises for implementing the np.newaxis for the get_angles instruction is not sufficient. The link for the np api as well does not include np.newaxis in its library. This assignment while very useful was disproportionately difficult.

Obviously, The professor and TAs have put a lot of time for preparation of this course, and I really appreciate it. However, the hws of the course is too much focused on language translation. They could put another examples, say business data, to represent other applications of RNN/LSTMs.

The course should provide more instruction on the Keras and Tensorflow, since the notebook is largely dependent on the knowledge of these frameworks. Moreover, the logic of programming is not so well-organized: I personally prefer to have my own logic instead of modules got implemented :)

Really interesting course with overview about sequence models and what can you do with them. Lectures from prof. NG are amazing as usual. The only thing I was missing was maybe more tutorials on Keras LSTM usage. The exercises on LSTM were quite confusing, especially using shared layers.

Very Important !!!

Hi,

We do need more programming assignments in order to master the material. We joined Andrew's courses to master (not just get introduced to) the materials, because Andrew and the rest of the team is awesome.

Thank you very very much for all your time and consideration

I am overwhelmed by too much material. Additionally Tensorflow and Keras syntax is not very elegant or coherent as they are such high-level languages. I learnt a lot at a high-level overview in this course, but my fundamental understanding was consolidated in the previous 4 courses.

Finally, the last course was completed. For me, this course is very difficult, because the content of the course is somewhat obscure and difficult to understand. But I learned some basic knowledge about Natural Language Processing and Speech Recognition through this course. Thanks!

Insightful course though I wish the course would be longer and more spelled out, since sequence models are quite complicated and harder to understand. Instructions on programming exercises are also sometimes pretty unclear, especially in later weeks. Still, overall a great course.

This is my third course in Deep Learning, the contents and pace of learning are great, they provide a good level of understanding in the subject. The notebooks have bugs and I wasted a lot of times making them work, thus they could be improved to use that time actually learning.

The concepts presented in this course were advanced enough. Yet, the assignments did not require much effort and thinking, which in my opinion is hurting the learning process. If students do not struggle enough with the course, they tend to forget the material they have learned.

Very interesting courses. I take this as a basis for future applications. I only regret that the exercises are too guided. I can't pretend to be able to accomplish a project in machine learning :-(

I would recommend also to note all the references to the papers, they are helpful.

Great content, assignments are fun and reasonably instructive (although they contain the occasional error and the video editing for the lecture content seems a bit rushed at times). I would recommend this course as an introduction to recurrent neural networks and related ideas.

Course is about recurrent neural networks, natural language processing and basics of speech recognition. Valuable content and great delivery by the author expect the final week where it's difficult to understand the transformers network and the related programming assignment.

Very informative and well taught course on sequence models. The amount of content and pacing was just right as not to be overwhelmingly complicated. There are a few bugs here and there in the programming exercise which can lead to a lot of headaches but overall a good course!

Great course, with interesting programming assignments, but still, I couldn't catch intuition about GRU and LSTM nature (I understood its pupuse and equations but couldn't get why exactly THAT combination of equations is necessary to allow RNN learn long term dependencies).

Week 3: quite a complected network was used for trigger word detection; however, it is not clear why exactly this architecture was used; specific order of dropout, batchnorm and GRU seems to be a pure magic; at least, a few words why this combination is picked are needed.

very good hands-on course. Yet I wished in the programming assignments, it was stated clearer, whether the implemented code is for understanding purposes only (and hence being the reason to be implemented) or is still mandatory even when working within a library (keras).

Course content is excellent, I would have given 5 stars, if the Programming assignments wouldn't have bugs. Fortunately people in forum help out with solving issues with assignments. I believe it's due to the short time frame the course is online and bugs get corrected.