Learner Reviews & Feedback for Sequence Models by DeepLearning.AI
About the Course
Top reviews
AA
Mar 3, 2018
Dr. Ng and team did a great job! Dr. Ng delivered even the most complicated concepts in the most lucid way possible. Assignments created by the team are awesome and very good to work on! 5/5 course!
JR
May 25, 2019
I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise
2901 - 2925 of 3,821 Reviews for Sequence Models
By Tất T V
•Feb 11, 2018
good
By Han C
•Feb 6, 2018
Good
By Nurtas K
•Mar 9, 2025
мрм
By Shakirullah K
•Feb 11, 2025
n/a
By 华卓隽
•May 13, 2019
666
By 莫毅啸
•Aug 3, 2018
ths
By 黄家鸿
•Jun 12, 2018
非常好
By 雷后超
•Apr 20, 2018
666
By Sylvain D
•Feb 12, 2018
top
By 杨天奇
•Apr 11, 2025
很好
By DuongTHQE180049
•Mar 5, 2025
ok
By Souleymane D
•Sep 8, 2022
ok
By Mohamed M
•Sep 27, 2020
<3
By Parth S
•Jan 3, 2020
kk
By Ming G
•Aug 26, 2019
gj
By Pham X V
•Nov 6, 2018
:
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By Amira K
•Jun 12, 2025
-
By Wassana K
•Jun 7, 2021
By Srikanta P S
•Apr 15, 2021
A
By Abdou L D
•Jul 15, 2020
-
By Jainil K
•Aug 11, 2019
-
By Musa A
•Jul 9, 2019
A
By 郑毅腾
•May 14, 2018
i
By wangdawei
•Mar 30, 2018
赞
By Mathias S
•Apr 22, 2018
The Sequence Models course was the one I sought out in the deep learning specialization. Very interesting assignments, e.g. neural machine translation, music composition, etc. - much more interesting than the convolutional network models, in my opinion. However, it is also much more difficult to follow; probably the most difficult one of the five courses.
Prof. Ng did a wonderful job in the delivering the materials, as always. However, I expected a lot more details about the sequence models, and recurrent networks as much as the ones given in the previous courses. I was looking forward to learn more in-depth about this model, but I didn't feel I get all that I wanted. For example, I wish there an example, step-by-step walkthrough of the backpropagation through time (BPTT) algorithm, especially for the LSTM and GRU models.
The assignments were a little more difficult to follow, I think. To me, the instructions were not as clear as the previous courses (in my opinion), especially when using Keras objects/layers - "use this *object/layer*" but it wasn't clear whether or not to fiddle with the arguments. Usually when it does require a specific value for the argument (e.g. axis=x), it will be mentioned either in the text or code comments. I guess it's a good challenge, but I find myself doing more trial-and-error with the coding to get it to work instead of having some guidance on how to use those Keras objects/layers. The discussion forums do help, however. Lastly, some of the assignments involved building a recurrent model using Keras layers, I felt like there was not enough explanation why such architecture, layers, or hyperparameter values were chosen.
Overall, I liked the course, I did learn a lot from the course, and enjoyed the models we get to play with in the assignments. I think I will still run into problems trying to devise my own sequence models, and fumble with Keras. I wish there is a more in-depth course on the sequence model. Prof. Ng's delivery was excellent; I enjoyed listening to every one of his lectures (even at 2x speed) :)
Thank you to Prof. Ng, and all the people who worked hard to develop the course.