Amazing content. Learned a lot. The math was a bit hard to keep up with at first but the intuitions behind them were well explained, so that the students can understand better.

Great for the most part, but the TensorFlow assignments felt flat and "incomplete." I ended up using Hands-On Machine Learning with Scikit-Learn & Tensorflow to bridge the gap.

There were a few errors in the final assignment and grading is very slow. That being said, the course was informative and provided some additional "tools" to add to my toolbox.

Good course, as the previous ones. I wish we had the possibility to download the slides or get some summary notes... Going back to the videos to check some infos is not handy.

A solid Part 2 to the deep learning sequence. My only issue here is that the final exercise emphasizes TensorFlow 1.0, which felt a little funny since TF 2 works differently.

The content of the course is really good and well presented. I would have appreciated more labs in the in-week material so to have more feedback on the practical coding side.

Like this course. It gives you good basic understanding of how to optimize you Deep Neural Network. But sometimes it is not so much practice. But this is not critical for me.

Tensorflow Version used in last assignment is old and syntax has been changed in tensorflow 2.0, it should be updated. But Overall very knowledgeable and insightful course.

The course help me to understand more clearly the optimizers as well as the process of

pyperparameter tuning. I think the assignments should be a little bit more challenging.

An excellent course, Andrew Ng is an interesting and knowledgable instructor. Better course summary materials are all that is missing, as they are mostly copies of slides.

Very nice course, but the exercises should be more complicated and longer.

How come the last week exercise does not deal with any regularization or hyperparameter training?

Kind of short, well explained, good assignments. The tensorflow video is for version 1 of tensorflow, so I was unable to follow along with it. Other than that, good course.

the course is focused on the main elements - it's a lot of material to work through - it would also be good to elaborate a project (could be an own one) during the course

This course made a lot more sense to me, compared to the first one. Still a bit excessive on calculus in my opinion, but I guess calculus makes more sense for other people.

It's comprehensive, even covering tensorflow. More details on tensorflow are suggested. Also the theory are more abstract and not quite well explained as previous courses.

Week 2 was very informative, Prof Andrew discusses the concepts behind gradient descent and momentum in detail. The programming exercises could have been more challenging.

The videos were great! Personally I was lacking some additional reading material and more quizzes. Other than that, the course demystified different optimization technics.

Very informative and explained very well. This course increased my enthusiasm and interest in ML further more. Looking forward to learn more about DL in the next courses.

A very good course. The current course content related to Tensorflow syntax detail (week 3) can be improved. Future coursework should probably use the latest framework/s.

A great course to learn how to make our deep learning models better. The flow of the course is superb. The only thing I felt can be improved was the level of assignments.

Good coverage of practical issues in hyper-parameter tuning, regularization, and optimization of algorithms. Would be better if it covers TensorFlow 2.0 (instead of 1.0).

couple of problems in notebook files (especially in the last homework) wasted my time, but it was a great course and to understand the math behind and learning tensorflow

Some of the Topics like tensorflow should be have some more explaination but even though the course is excellent and as far as for Andrew Ng ,he is best among the bests.

Learning a lot. But full of boiler plate codes. It would be great if students were challenged with programming. Writing a formula even in code is easy for most students.

The content is good but I didn't feel comfortable with tensorflow assignment. That could be improved by stressing more on tensorflow syntax and explaining it in detail.