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

It might help the academic learners if tutorials can be more engaging and rigorous than they are at present. But hey, not bad at all! :)

It's really great Mr/Andrew has a good way of explaining stuff even tho i need to search some stuff on youtube for greater understanding

I loved the course, as it provided concise explanations and covered all important topics required in Deep Learning. Thank you Andrew Ng!

Would have been 5 star but I found typos in the assignments and exercises -which have still not been corrected which is quite surprising

Good course overall . The batch normalization explanation were not so convincing. The last assignment on Tensor Flow need improvement.

The course is nice but the tensorflow exercise has a lot of functions that weren't explained before and implementing them was difficult

Its good hands-on course but to master it will certainly requires to dwell more into the specifics and need to work on various projects