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

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.

This is another great introductory level course. Andrew covers a lot of very practical concepts. This course also builds well on the previous course in the specialization. The only reason I gave 4 stars rather than 5 was that the programing exercise still uses TF 1.

A good theoretical explanation and good working assignments that impart basic understanding of different optimization methods, hypertuning methods and tensorflow framework. But, some mistakes in the tensorflow assignment in the script itself, needs to be rectified.

Andrew Ng presents a very organized course; I would have enjoyed actually iterating on hyper parameters to find the optimal set. Also, there are probably other optimization approaches that would enable simultaneous searching for an optimal collection of parameters.

Overall, very clear teaching from the Great Mr NG, only concern is that the markdowns in the assignment three coding assignments have some mis-aligned expected output from what is actually expected, so there is some confusion. Except that, all is great. Thank you.

It was very helpful to learnt batch normalization, regularization and tensorflow. It definitely needs a lot of self study to learn about these topics for people who are not familiar. Some mathematical resources like links to pdfs and videos would be extra helpful.

I believe we should extend this course for another week to properly cover TensorFlow. We end up copy / pasting code in the assignment without fully understanding the entire code. Otherwise, the quality of the course is always good, thanks to Andrew :-)

Thank you!

Super interesting course where you'll have to come back a few more times because of the density of the theory. It's overwhelming the amount of hyperparameters you need to tune, but it also makes it challenging and less boring to set up neural networks and models

There are a few errors in the programming assignments, which caused some confusion. Finding these errors was a useful exercise, but it would obviously be better to have a debugging problem or two rather than errors in the problem hints or expected results. :)

The lectures are great - but the Jupyter notebook assignments are hell, as they they crash frequently and most of the time spent on the assignments is invested in dealing with the notebook instead of the exercise. (The content of the exercises is great though)

The quality of the information is awesome. There are some minor bugs in the assignment section. Even though you have submitted the right answer it shows that you have secured 0 marks in that section. Apart from evaluation bug this course it super knowledgable.

There were a few mistakes in the output of the comments of the notebooks,Also sometimes my output did not match the expected output,still the assignment got graded correctly.Eg-The tensorflow notebook.I think it was because of the seed of the random processes.

Well-organized course. I gave it a four instead of a five just because the Tensorflow HW is not as good as other HWs. There should be more comments and more examples. Maybe there should be two HWs on Tensorflow to give me the confidence that I have leaned it.

I would rate this 4 for the following reasons:

1) Learnt all the optimizations.

2) Hyper Parameterizations

I would not rate this 5 for the following reasons:

1) Some more time could have been spent on tensorflow.

2) The assignments were just simple substitutions.

Great explanations on behind the scenes operations of optimization algorithms and general theory. Coming from a more practical background, it helped me grasp the concepts much better. I only wish the programming exercises were a little bit more challenging!

Generaly it was good as expected! But the problem with this course was the programming assignment at week 3, where it did not include programming training for most it's content which where Hyperparameter tuning and batch norm and was all about TensorFlow!

It was a good course, with giving a great detail on tuning the Hyperparameters. I personally didn't myself found it useful as of now, but the course was good, and can be recommended to other people to fine-tune their networks. Jumping on the third course!

Programming assignment was more difficult but the Course itself really built on the first course well. I struggled much less with the material and enjoyed it more. I also appreciate it being shorter despite having to restart because I had gone on vacation

I liked everything except the tensorflow part of the course. That was too quick, and the last assignment IMHO was not very useful. I did it mechanically just to complete the course. A full framework course is perhaps useful as part of the specialization.

Overall very good. Many of the useful concepts did not have practical "coding" assignment.

It would have been great to have the opportunity to see how many of the tuning, regularization and optimization techniques can be mixed in a real world scenarios

The course teaches you well on how to optimize your neural network. The only problem I had was with week 2's programming assignment because the grader had problems with the "-=" operation. The lecture I enjoyed the most was the Adam Optimizer lecture.

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).

This course taught me a lot of new concepts and tricks to speed up the training process as well as ways to reduce overfitting and biasing in a neural network. I would've liked the course even more if the instructors took a deeper dive in frameworks.

I learned a lot in this course but I feel like the assignments should be little big and less informative. The assignments are designed are good for then who are at base level but too short for someone who had their hands on once in neural networks.