Jul 01, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
Mar 14, 2018
I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!
por John S•
Feb 03, 2019
Interesting and full of excellent lectures as always for Andrew Ng. The programming assignments quality was not as good as the other courses in the Deep Learning specialisation though. They drop straight into Keras with no information/introduction, use several complex model architectures without explanation, in week 3 4 out of the 5 'your code' exercises were about audio sampling, not very relevant. Again, excellent lectures, just not great programming examples.
por Wolfgang G•
Jul 12, 2018
Sorry to say they dropped the ball on this one. The last course of this specialisation has the most advanced topics thrown at you in just three weeks, and it's even more cookbook-like than in the previous courses. The material of this part of the specialisation would require a whole course in itself, perhaps for +10 weeks. Here, I found it is at best a guide for self-study, _if_ you have the time for that. Also, support in the forums was very minimal.
por Bradly M•
Apr 17, 2019
The scope of this course was highly relevant to me, but unfortunately many of the class materials were broken or otherwise incorrect, making some ungraded portions of the assignments difficult or impossible to achieve. Activity on the discussion boards indicates many people have tripped over this for at least the better part of a year, but no corrections have been made. This was quite frustrating and wasted a good amount of my time.
por Yevgen S•
Jul 22, 2019
I took this course after a long pause after I finished the first 3 courses. I would NOT recommend doing it that way. As a result, I felt rusty on some of the coding practices.
I think the course gives great introductory information on RNNs and LSTMs. The first two weeks of the course are spot on. However, I think the third week is lacking. I had hard time making a connection between the lecture material and the assignments.
por Adam J•
Dec 02, 2019
This course was at a really high-level and barely scratches the surface of Sequence Models. Didn't really go into much detail behind any of the theory, and the programming assignments were mostly done for us, so you don't really end up learning much. You certainly won't be ready to have a job solving NLP problems after taking this course. If you want that, you're better off going through actual college courses online.
por Eero L•
Jun 07, 2019
The course content and Andrew Ng are great. The submission process of the assignments is absolutely dreadful. You might get 0 points for correct answers or not, depeding on...well, I have no idea on what. Maybe it's Jupyter Notebook, maybe it's Keras or maybe it's something else. But you must have good search engine skills, since you will most likely spend a lot of time in searching the discussion forum for answers.
Feb 19, 2020
too much information for such a short course. We only get a very superficial understanding of concepts with very little practice to solidify our understanding. The assignments involve implementing very small parts of much bigger systems. I guess the course is ok to get a general idea of the concepts but for deeper understanding of the topics a longer course or multiple courses would be needed.
por Aliaksandr P•
Mar 31, 2018
This is a very interesting topic. However, I believe the course itself can be improved. I believe there can be more information about NLP and sequence models in lectures. It would be nice to add lectures with practical suggestions about training and tuning sequence models. There were lots of typos and mistakes in notebooks that were found by other fellow students and not addressed by mentors.
por Heyang W•
Feb 19, 2018
The course overall isn't as good as the previous 4 ones especially for the PA part, I can pass the grader even with wrong output. The PA improvement sometimes just create more discrepancy. The PA is just a walk through of how to building those basis models, but those little bugs will drain extra hours to figure out. I think this course is kind of a prototype one especially on PA part.
por Peter F•
Feb 20, 2018
Compared to the previous courses, this was a disappointment. There is not as much content as I expected and the homework exercises are not well prepared. If one spends more time with debugging than with "learning concepts" in a basic course like this, then something seems wrong.
Moreover, in a situation where so many people pay so much money (because of Andrew Ng's credit)...
por Vivek G•
Dec 27, 2019
That was tough, how the weights are stored and their dimensions inside the 'time steps' can be explained by adding one more video, btw the course is awesome if you want to learn the basics of sequence models, you should have completed the previous 4 courses before diving into this. I will always remain thankful to Andrew Ng for providing this type of platform.
por Odinn W•
Jan 13, 2019
Positives : Excellent lecture material. Assignments broadly are well structured. HIgh bar set by Andrew Ng. Negatives: Assignments have too many errors and mistakes as of Jan 2019 (especially but not only in the optional / ungraded sections) for me to be confortable 100% recommending the course. Instructions for assignments are also not fully fleshed out.
por Sumandeep B•
Mar 30, 2018
This course is good for introduction to sequence networks, but I felt this is not at par with the previous course 4 (CNN). This feels a bit hurriedly done, with many important things only just touched upon. This should have been a 4 week course like the previous module. Then due attention could have been given to the field of speech, audio, sequence domain.
por Krzysztof J•
Feb 04, 2018
The course is generally good. However there are some issues with lecture videos editing (some sentences are said multiple times), and with activities (e.g. default settings hardcoded in one of notebooks, didn't let have output shown as reference, also in some cases automated grader has some assumptions, which need to be found using trial and error method).
por Jérôme B•
Feb 19, 2018
I've got mixed feelings about the whole Specialization. Many very interesting topics, but on the other hands I don't feel like there's any takeaway knowledge for me. Until the very end I've been feeling completely lost in the exercices. I'm proud to have been able to hold on until the end but I'm not sure it's been an useful use of my time.
por Aditya B•
May 09, 2019
Really interesting course with fascinating applications. However, in terms of difficulty, it is a significant step up from all the previous courses. A lot of time is spent figuring out the syntax even though the concepts are crystal clear. ( Probably as it is a collaboration with NVIDIA). The programming assignments could be improved.
por Miguel O•
May 24, 2020
It´s a fairly good course, with lots of cool topics covered on it. My main complain would be that the subjects covered are dense enough to be arranged on a four or even five week course. Instead, for some reason, all the stuff has been squeezed within three weeks, which makes the lectures shallow and rather cryptic most of the time.
por Romain L•
Mar 25, 2019
The course was great, as ever. But some of the programming exercises were very frustrating. Oscillating from very easy to very difficult, with some unclear (and sometimes erroneous) instructions. I felt this was in sharp contrast with the previous 4 courses of this specialisation, for which the course and exercises were perfect.
May 01, 2020
the course covered a lot of essentials and gave me a rough idea of how stuff NLP and sequence models work. Though at the same time the content often left me confused and overwhelmed. the Convolutional Networks course was far better.
Overall its great work and I am thankful for hard work put behind the complete specialization.
por Hans E•
Mar 03, 2018
Great lectures, great teacher!
I would have given 5 stars but for the problems in the exercises / grader. Some problems that are know for weeks or even months are not resolved. This causes many wasted hours for many hundreds of students. Please solve this and make it a 5 star course.
Many thanks to Andrew Ng and the mentors!
por Glukhov E•
Feb 17, 2020
The programming tasks were very simple. I doubt that you can really learn anything when you just need to copy the text from the task description and paste it. The content of the tasks was excellent, but the level of personal involvement was minimal.
In addition, the information in the course is already outdated.
por Richard S Z•
May 17, 2018
The lectures were OK ... better LSTM tutorial by Chris Olah
The exercises really need some review ... very frustrating ... and not all that illuminating .
The course was a good intro to DNN ... but I think either replace Week 3 - Structuring ML Projects with a course on Keras ... or add a course just on Keras.
por Piotr D•
Nov 17, 2018
The course does not explain how to use Keras (it's assumed you've finished the previous course). What's more a lot of code parts is implemented in some difficult way (for loops instead of Python's builtins and idioms like any or list comprehensions). I'd love to see more materials on speech recognition.
por Suresh D•
Mar 26, 2018
I guess as the subject matter becomes more complex, more training is required on the underlying frameworks being used- Keras, TensorFlow etc. Did not feel that sufficient time was spent on understanding the underlying frameworks. Also the TA work is of spotty quality. But I love the way Andrew teaches.
por Salih T A•
Apr 05, 2020
The assignments were not good i think. Because they explained the consepts too long and complicated as like we've never seen these on lectures. I was waiting assignment to require more insight about architecture and less python programming knowledge. This comment is for week1 assignments in special.