Oct 30, 2018
The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.
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!
創建者 Georges B•
Feb 21, 2018
Great course and material, Andrew NG really know who to explain difficult subjects in an intuitive way. However, the course seems that it still needs some work (there are some bugs in the lectures and assignments)
創建者 Seungjin B•
Sep 08, 2018
Week1 lessons are a little complex than the previous classes and there are gaps between ground-up python version and keras version of LSTM model. Keras will need to be taught a bit more in detail to follow up.
創建者 Lester A S D C•
Jul 29, 2019
This is by far the hardest course in the specialization. But it was explained well. My only complain is there were errors in the first programming exercise. All in all, I learned a lot in this specialization.
創建者 Guoqin M•
Jul 02, 2018
Great content! I really love Andrew's teaching style. (1 star deduction for some programming assignments where I spent time debugging but it turned out that the point deduction was due to the grading system.)
創建者 Divya G•
Mar 25, 2019
The programming exercises are a little heavy in this course where we need to load and re-load for them to give correct output even if the code had been correct all throughout. Otherwise, the course is great.
創建者 Mathieu D•
Sep 10, 2018
4 stars and not 5 stars because the course is shorter than the others and it feels like an exemple in classical forecasting is lacking (sales, time series...).
Really interesting but may be too focus on NLP.
創建者 Zhaoqing X•
Jul 25, 2018
It's an excellent course! I will give it 5 stars if it could offer more interesting and meaningful assignments(Not offend, but it a little too easy and the assignments are not very related to the real work).
創建者 Ayush N G•
Sep 23, 2019
The course should contain more explanation about natural language processing like tf-idf,lemmatization,stemming,dialog flow. Although i got a good explanation of working of RNNs,LSTM and machine translation
創建者 Md Z S•
Feb 04, 2019
Great course to start off with sequence model. The programming exercises were in depth and deliver a great learning experience. Would love to see more of sequence literature in the course's future versions.
創建者 Michele I•
Apr 18, 2018
Again a brilliant course from Andrew NG, but though and dense this time. In order to grasp the meanings videos and lectures need to be revised a few times. Also, get some extra info elsewhere does not hurt.
創建者 Aleksi S•
Feb 22, 2018
Excellent presentation, and interesting assignments. One star dropped because a couple of technical issues with the assignment material (typos in the mathematical formulas / expected results here and there)
創建者 Zhao L•
Jun 09, 2019
The contents are great as always. However, the server is not reliable. Once, the grader is down and you can't submit homework. For another time, the connection is lost and all the changes made are lost.
創建者 Eoin T•
Feb 20, 2018
Great course, but I felt the gap between the very high level lectures and very low level labs was a bit too wide. I had some issues with the autograder and losing progress in the notebook between sessions.
創建者 Angelo C•
Jun 24, 2019
Very well produced and explained. In my case, the nature of the Sequence Model makes understanding the concepts and finishing the assignment more challenging than other segments of the specialization.
創建者 ROHITH R E•
Feb 22, 2019
The course is very short when compared to first 3 courses in this series. It would have been better if more explanation and shorter assignments were provided in the initial weeks and increase the pace.
創建者 Prashant J•
Apr 04, 2019
The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!
創建者 Balazs A•
Jan 15, 2019
The material itself is very informative and useful. But I have to give "just" 4 stars because, the training videos have to be edited better and there were a few mistakes in the programing exercises.
Dec 15, 2018
Please work on getting the notebooks to work properly. Also very bummed that after canceling my subscription, I won't have access to my homeworks. You guys should give us lifelong access - we paid!
創建者 Yen-Chung T•
Feb 21, 2018
A general overview into the power of sequence models. There is no rigorous mathematics here so most of what students can learn is high-level implementation and intuition about the various models.
創建者 Ali K•
Mar 24, 2020
An appropriate course for getting started with Recurrent Neural Networks and its very applications in the domains of speech recognition, sentiment classification, neural machine translation etc
創建者 Tina W•
Jul 29, 2019
Very useful materials, but a little hard to digest. It will be more helpful if course lectures and slides can dive a little deeper into each model and provide more granular technical details.
創建者 Kaushik A D•
Jun 17, 2019
Exercises are sometimes vague and can be done without full understanding of the material. No officially given notes. No reading material that explains the concepts more clearly and in detail.
創建者 Julian R•
Feb 17, 2018
Great Course, very insightful excercises, really enjoyed it - unfortunately some issues with the programming excercises.
Since it's the first run of the course they should have fixed it soon.
創建者 Sharvil G•
Apr 29, 2020
the course content was really impressive, but some topics need to be explained in detail. Also working of LSTM should be further described in detail. Programming assignments are really fun.
創建者 Bhargav A•
Dec 25, 2018
great course, lots of implementation details that are important were glossed over, while other areas had too much hw hand-holding + strict requirements on what the code needed to look like.