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.
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!
By Franck B•
Really big struggle with dinos, versions of workbooks, and sometimes no logical way to explain why grader does not validate a working notebook. Pain, frustration, taking away time from proper learning.
On the course itself, some exercices felt like toying (e.g. very simple function to check if a time_segment already exists) in the middle of a keras deep learning model, where learning debugging, setting up smaller ones would have helped me learn more I think.
Still not sure I am at ease with creating models, we experimented various ways over the specialisation, and the selection of model architecture or even tuning after 1st running version is still mostly guess work to me. Will need to digest and keep learning
By Benny P•
This course provides great introduction to RNN and other sequence models and their application to popular fields such as NLP and audio processing. It does great job in providing the motivation and intuition behind the creation of such sequence models (e.g. LSTM, GRU, Word2vec, GloVe), however I feel that the theoretical explanations need to have more depth. During this course I had to refer to other websites to gain more technical understanding about LSTM and GRU. The programming exercises are nice, they cover many popular topics such as NLP, speech, and music processing, but I struggled when doing it in Keras. I wish some pointers were provided on where to learn it before doing the assignments.
By José A M•
Too many stability issues on the platform to get the notebook up and running.
Few bugs and errors on lectures and exercises, if they are found by the community you should update the material even if it involves recording a video again. Too much time spent on the notebooks figuring out "side" stuff that is not what I am here to learn.
While on the course for CNN it covered the state of the art of the field, in LSTM I think there is much more that could have been explained.
I have missed examples on other type of problems like forecasting time series, events and other more business like applications.
Still I learnt a lot and would do it again.
By Felipe M•
Although the course content is very useful, the hurry in which the course was put together does show. Video was clearly under-edited (as is apparent by Andrew repeating some statements in the expectation that the previous one would be edited out), and the auto graders caused me to waste many more hours than truly needed to get my assignments in a format that would be accepted. Finally, I was very disappointed at the fact that the specialization was launched and then the last course pulled out, so I had to pay two months even though I had budgeted my own time to finish it in one.
By Arjan G•
Nice to learn how RNN's work. But too rough around the edges for a 5-star score.
I learned RNNs, language models and many other useful techniques
Subject matter is mostly well explained in the lectures
Original authors of a technique are cited
Some things should be explained more elaborately while other explanations can be shorter, especially in the assignments.
Mistakes in the editing in the audio clips of the lectures
Mistakes in the notebooks, sometimes non-intuitive/bad coding principles are used
By Gautam D•
To be completely honest, I loved Dr. Andrew's method of teaching. But the assignments just flew over my head because I didn't have enough hours of practice of Keras under my belt. I know Keras is there to make things easy but it's very difficult to just trying to pass the grader. To goal of assignments was fantastic, I mean, generating music, etc. sounds really amazing but I feel that if there was some more time given to make us better in Keras and other technicalities then I would've loved this course much more!
By Javedali S•
Good but i expected more. The main thing i like about first 3 courses, they were really deep. In the last two courses we have skipped the backpropogation. Now this is something which you can keep optional. I like the way Andrew Ng teaches, going to the basics, and that is why I came here and paid 40 euros per month. Also, there are few stuff missing like Generative models, Adversarial networks, GAN and etc. It would be good if Andrew can have more courses related to this and deep (as it is deep learning :))
By Kush S•
By far the most difficult of the 5 courses but giving it a lower review since the programming assignments are rushed through to finish 2-3 in 1 week which gets hectic & understanding of key concepts is lost. Also, it would help if more time is spent in the videos to explain the concept/model/algorithm used in the assignments since I close to understood nothing from the assignments in spite of completing them. Finally, the instructions too were not clear in the assignments.
By John S•
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.
By Wolfgang G•
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.
By Bradly M•
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.
By Yevgen S•
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.
By Adam J•
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.
By Md. B U A•
First of all, the programming assignments are really copy-pastes. There is nothing really to storm your brain for. Second, many of the ideas presented in the video lectures are very brief and short, skipping the explanation parts. After taking this course, I now know the names of lots of algorithms and models, but that's all I know, only the names. To get broader knowledge on them, I have to look somewhere else now.
By Eero L•
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.
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.
By Aliaksandr P•
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.
By Heyang W•
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.
By Peter F•
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)...
By Vivek G•
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.
By Odinn W•
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.
By Sumandeep B•
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.
By Krzysztof J•
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).
By Jérôme B•
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.
By Aditya B•
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.