It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.
While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).
By Mayur S•
The course material can be clubbed with existing courses. It would have been much more meaningful with some examples and hands-on assignments
By Rindra R•
Covered important topics and real-world project considerations. However, the content and assignments are too short to make it a full course.
By Daniel K•
This time it was not that well-structured than the previous courses. I thought we would learn how to structure step by step an ML project.
By José G•
Lots of information, few knowledge
Change name to "Struc. Deep Learning Projects", all other forms of ML not considered, specially for P2.
By Eric K•
Too much similar material to the prior course, and only two simple quizzes, no hands-on programming assignments like in earlier courses.
By Eric M•
A fundamentally very good course with a few technical gltiches that can be easily corrected and some confusing elements to be clarified.
By Bongsang K•
I think this lecture is important for every research scientist. However, there was no programming examples so I was confused sometimes.
By Michael L•
No programming assignments or labs, so too much theory, and too little chance to put same into practice. Not a good value for my money.
By Max S•
Still good but getting much sloppier. Bad editing of the videos, some exercises plain wrong and staff not reacting to forum posts, etc.
By Xiang L•
This session might not be very helpful for people from different backgrounds such as non-industral level application of deep learning.
By Lars L•
Course materials need some cleanup. Were a number of audio blips, in the video. Material was good but just didn't seem as polished.
By Nitin S•
Decent learning. Though quite some stuff, I felt as repetitive and obvious.
I wish there was some programming exposure as well here
By Taavi K•
Too short on its own (took half a day to go through the whole thing), could have been combined with Course 2 of the specialization.
By Jean-Michel P•
I feel like this course should be broken down and included in the other courses to get better context within these other courses.
By sai r t•
this session was good it would be more better if they provided the code of them..so that we could be abke to learn more from them
By Denys G•
Felt a bit rushed, each video was full of good tips but personally I think each video should have been a jupyternotebook instead.
By Massimo A•
More theoretical than the other courses in the specialisation but still very high quality.
Short but with a lot of information.
By David P•
Not nearly as good as the first two courses. These two weeks should probably be added into the second course at some point...
By Oliver O•
Would like more applied discussion and for it to be Longer. In particular I would like to see a discussion on class imbalance.
By Shuai W•
The content of this course is a bit too little for me.
However, it provides useful guidance for my projects. Much appreciated!
By Gary S•
Not nearly as valuable as the first Deep Learning course. And the questions posed in the quizzes seemed far more subjective.
By Pejman M•
Programming practices with TensorFlow should have continued in this course. Unfortunately, these two weeks were all talking.
By Nithin V•
Need more quizzes, assignments to deepen the understanding, But otherwise thank you Andrew Ng for presenting this material
By Panos K•
The pace of the first part of the course was too slow. The second part (from Transfer learning onwards) was much better.