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.
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 Sravan
•Works as a primer. Assignments aren't that great.
創建者 Jerry Z T
•The learning embedding part is kindof confusing
創建者 Prashath M
•Excellent content
poor support from the website
創建者 Abhishek S
•Great course but has been dumbed down too much
創建者 Yue E
•Esperaba que los ejemplos fueran de otra forma
創建者 Jazz
•Should add some instruction videos of Keras
創建者 Shanger L
•does HW created/reviewed by different ones?
創建者 Parikshit D
•The assignments are not very satisfactory..
創建者 CLAUDIO G T
•Not so well explained as the other courses
創建者 Xueying L
•Too narrow focusing on applications in NLP
創建者 Rahul T
•Programming exercises was very confusing.
創建者 Ritesh R A
•Course should have have more descriptive
創建者 Liang Y
•Too many errors in the assignments
創建者 guzhenghong
•The mathematical part is little.
創建者 julien r
•second week was hard to follow
創建者 stdo
•So many errors need to fix.
創建者 ARUN M
•very tough for beginners
創建者 Wynne E
•Keras is a ball-ache.
創建者 Long Q
•too hard
創建者 CARLOS G G
•good
創建者 Debayan C
•As a course i think this was way too fast and also way too assumptive. I wish the instructions were a bit slow and we broke down more into designing bilstms and how they work and more simple programming excercises. As a whole i think 1 full week of material is missing from this course which would concentrate on the basic RNN building for GRUs and LSTMs and then move on to applications. I usually do not review these courses and they are pretty standard but this course left me wanting and i will consult youtube and free repos to learn about it better. I did not gain confidence on my understanding. Barely scraped through the assignments after group study and consulting people who know this stuff (which defeats the purpose of this course i believe. It is to enable me with concrete understanding and ability to build these models . It shouldn't lead me to consult others and clear out doubts .)
創建者 象道
•i really learned from this course some ideas on recurrent neural net, but the assignments of this course are not completely ready for learners and are full of mistakes which have existed for more than a year. those mistakes in the assignments mislead learners pretty much if they do not study some discussion threads of the forum. this course has the lowest quality among all of Dr. Andrew Ng's. before the updated versions, a learner had better have a look at the assignments discussion forum before starting the assignments.
創建者 Luke J
•The material really is great, but work needs to be done to improve the assignments, specifically submission and grading. On the last assignment I spent way more time troubleshooting the grader than the content of the assignment. It can be very frustrating to have to do this on a MOOC where no human support is available. It appears, specifically for this assignment based on discussion that this has been a problem for a very long time.
創建者 daniele r
•The subject is fascinating, the instructor is undoubtly competent, but there is a strong feeling of lower quality with respect to the other 4 courses in the Spec (in particular the first 3). Many things in this course are only hinted to, without many details. Man things are just said but not really explained. Many recording errors as well. Maybe another week could have helped in having a little more depth in the subject
創建者 Amir M
•Although the course lectures are great, as are all the lectures in this specialization, some of the assignments have rough edges that need to be smoothed out. It is particularly frustrating for those trying to work on the optional/ungraded programming assignment sections that have some incorrect comparison values, as much time will be wasted trying to figure out the source of the error.