I missed in the course an explanation and possibly a programming assignment of different tuning algorithms, such as random search and Bayesian search.

The coverage on the subject was adequate but it will really help to make a pdf supporting document to highlight the hyper parameter tunning approach.

Probably a little too follow your nose at this point in the specialisation. But none-the-less very good. Would give 4.5 stars if that were an option.

Still some flaws + inaccuracies + video sequences that should be cut out. I think the organizers should really do it as people are now paying for it!

Last Tensorflow assignment has some output typos and bugs when using operators like @ and +. Course was ok, but that assignment took me way too long.

Great course to deepen my knowledge after first course. However, I would like to access more programming exercise for practice. That will be perfect!

I think this course would benefit from a little more explaining. There are a lot of new concepts and some explanations were too quick in my opinion.

The introduction to TF felt rushed, but maybe that's to be expected given the goals of this Course, which didn't include a kickstart of learning TF

A very good course but I had high hopes for a practical session (Assignment) with application of hyperparameter tuning, with Tensorflow or without.

Теория хорошая, но практики во первых мало, во вторых на устаревшей версии тензорфлоу. Но зато в теории все разжевали. Очень круто все объясняется.

Enjoyed this course, especially the material that goes a bit deeper (different optimization methods, parameter tuning) and the intro to TensorFlow.

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!