Less exciting than the first course, but this course is important to understanding the parameters that could affect a neural network's performance.

I feel like the course is a bit too easy. I would recommend making it more difficult by letting the end-user try more hyperparameter tuning tests.

what I did recognize in the deeplearning specialization that there are now further reading suggestions or reading syllabus like the other courses.

This course is one of the best course for good understanding of hyperparameter tunning.

And also let you know the effect of various hyperparameter.

Nice course, TensorFlow might need some more in-detail explanation because it's a different than programming with Python, but it was really nice.

Nice course and nice the Tensorflow introduction, but there are errors on the lecture and on the final test. Be aware to use the forum some times

Course was awesome, but there is an error with the grader for one of the programming assignments that took some time to search for a workaround.

Valuable lessons, but the tensorflow lesson + assignment at the end was a bit vague and hard to follow to the point of passing their test cases.

It's very good, but I would have spent more explaining the difference between adding layers and adding neurons, and how to decide the next move.

a few of the examples and expected output for the programming exercises seemed not to be correct. otherwise great course. highly recommended.

Good course. Kinda skips over hard bits which only leaves one with more questions. Hopefully these details are recovered in the later courses.

Very good class. Appreciate it.

However, the explanation for some theories is not enough.

More explanation needed for Adam optimizer, RMS prop.

It's a wonderful course because it provides me how to improve deep neural networks and delve to some techniques to gain good hyperparameters

Would have given 5 stars if the Jupyter exercise did not give me too much of a hard time looking for errors in syntax. Overall, great lesson!

Loved the easy to understand explanation given by Prof. Andrew Ng for some of the most complex concepts in Deep Learning like Regularisation.

This class is very helpful for understanding parameters of ML except week 3 class and assignment for Tensorflow which is not fully explained.

The TensorFlow part of the course could have been more in depth, because there were lots of problems faced during the programming exercise.

I don't understand tensorflow mechanism and when to use what function. Should I stop to learn more tf or just move on the following courses

The video content and theories went very how. However, week 3 assignment has some bugs and unclear explaination of compute_cost exercise.

Quite ok, but programming assignment was mostly copy-paste style. I am not able to repeat something similar independently after the course

A lot of great concepts covered in the lectures but only few were explored in the assignments. The assignments seemed fairly simple to me.

There were grading results that seemed wrong - I've submitted report on grading to explain details. Other than that, the course was great!

This course was nice for me.First Andrew Ng talks about mathematicall background of the concepts then you get hands on coding experience.

Good course. However expected some more mathematical proofs for some of the ideas like bias correction and exponential weighted averages.

Very good course. All the concepts explained very well. I just feel programming assignments were too easy, they could be a little tougher