There are so many grader problems please fix it

Need Information about other parameters like #of iterations, how to choose number of hidden layers?, number of neurons in hidden layers, inclusion of few other strategies to choose neural network model will be helpful. If they are covered in next courses, then please ignore.

Thanks

The courses are vey well organized and easy to understand.

One of the best and most technical course in this Specialization: I enjoyed learning a lot on optimization algorithms. Really good practical hints on tuning and on bias variance analysis, that are very difficult to find in textbooks

Great explanation on tuning different hyper parameters and how they can effect the model's performance.

Some assignments, even though I get the same result as the output given, it get marked as wrong... Please try to rectify it

Great, but could be better. Fix the typos. Add summarized video notes. Etc.

A bit too technical for my taste. But useful examples to work through.

The course was very helpful as now I understand optimization techniques and all the parameters of neural networks. Unfortunately, the course has not answered my question how to tune the whole bunch of hyperparameters from the scratch, what is the correct order and logic of the full ANN tuning, not just one parameter.

This is a very interresting course that go past basic deep neural network knowledge. I learned a lot. Still I would have like a bit more programming exercices to have more part of the theoretical course covered (batch norm, hyper parameters tunning).

All is good except the submission part, sometime return submission failure without specifying a reason

Last code assignment is a mess. Looks like organizers have no intention to fix errors.

Felt fast faced. But a good introduction to neural network hyperparameter optimization.

Good Insights of hyper parameters with other techniques to improve learning rate.

The Jupyter notebooks had a lot of mistakes which wasted a lot of my time otherwise the course content was good

Grader is not performing well even though my outputs are matching.

Interesting material but a bit complex to follow all the equation derivation. Need to repeatedly watching the video to understand the content. After learning this the hyper parameter setting in the ML setup is clearer to me.

good

No course material available

Decent intro to tuning neural networks. I felt the parts on normalization and regularization could have gone into more detail, but perhaps the math was deemed too complicated. Labs are ok, but still a bit buggy despite errors being reported in the forums a while ago.

Cours intéressant merci beaucoup Mr Andrew Ng

Extremely well designed course, the key reason for 4 stars is Andrew Ng's amazing leactures. The programming assignment though do quite a bit of handholding which can be reduced.

Amazing experience overall!

Great videos but wish there were more per-lesson exercises that were there in Course#1 for this track. Also, the transition to TensorFlow was quite abrupt as the key concepts that TF uses are completely new and don't easily borrow from the much cleaner Numpy concepts

The content of the Course is very precise and assignment truly reflect what is been taught in the lectures. Explanation and presentation of algorithms are what I like the most. Assignment were very engaging and interesting.

Sometimes quit slow