Not so much different with the materials in the Machine Learning course from Prof. Andrew Ng itself. If you don't have the time to finish the ML course, then you should take this one.

Great content but there are major problems with the final assignment. The one-hot encoding function tests force the output tensor dims to be different than what a later cell expects.

The mathematical explanations were very good. But the coding task is always left to do at once. If it can be set after the corresponding videos as a module it would be great I think.

It is an excellent course. The only weird thing it is that it uses Tensorflow 1 instead of 2. I get it why is it done, but perhaps it would have been more useful to keep using numpy.

Awesome material, and everything is well explained. I would've liked that the programming exercises were a little more challenging, though going through the code shines a nice light.

As always Andrew Ng's clearly explains all the concepts along with practical programs. I would strongly recommend doing this course for a good solid understanding of neural networks.

While video lectures are very well explain subject matter, practical assignments are pretty frustrating since most of the time you will be battling jupyter notebook and auto grader.

Very good course, although it'd be awesome if Andrew went over the backprop associated with Batch Normalization and perhaps a programming example of using Batch norm on my test set.

I loved the course but the tensorflow implementation was a bit weak, it passed in just 15 min video. I recommend this course but as i told before tensorflow migration is a problem.

The graded functions could be a bit more free form, forcing you to think more about it. I sometimes feel that I'm more solving the "template", than I am thinking about neural nets.

Personally, I would like to have more programming exercises on the things that are taught (Hyperparameter tuning, Regularization) in order to compare how different techniques work.

Just enough explanation of material to get started on using DNNs for my own tasks. Assignments are easy, though provide good explanation of what is occurring in each line of code.

It's okay. It's get a bit hairy with all the notation and varied intuition, but it follows suit and is not impossible to understand! Thank you Dr. Ng, I look forward to more.

Ugo

The course videos are very well organized and easy to understand. I would like to see more coding exercises, and a little more in-depth explanation of the Tensorflow/Keras API.

You learn about improving your capacity in the modeling and logic in your neural networks. This course is full of tips and tricks very important in your career in deep learning.

explained very well, interesting and engaging assignments, sometimes I get lost with all the mathematical representation and details but overall very good course I recommend it.

Everything was great. Every peace of information scratch in my mine. I learned a lots from course.

In the last part; Tensorflow has dramaticly changed but content didn't renewed.

everything you need to do is given in form of the hint. That was a nice course in terms of the explanations, but too easy in terms of applying it in a programming assignments.

Amazing content. Learned a lot. The math was a bit hard to keep up with at first but the intuitions behind them were well explained, so that the students can understand better.

Great for the most part, but the TensorFlow assignments felt flat and "incomplete." I ended up using Hands-On Machine Learning with Scikit-Learn & Tensorflow to bridge the gap.

There were a few errors in the final assignment and grading is very slow. That being said, the course was informative and provided some additional "tools" to add to my toolbox.

Good course, as the previous ones. I wish we had the possibility to download the slides or get some summary notes... Going back to the videos to check some infos is not handy.

A solid Part 2 to the deep learning sequence. My only issue here is that the final exercise emphasizes TensorFlow 1.0, which felt a little funny since TF 2 works differently.

The content of the course is really good and well presented. I would have appreciated more labs in the in-week material so to have more feedback on the practical coding side.

Like this course. It gives you good basic understanding of how to optimize you Deep Neural Network. But sometimes it is not so much practice. But this is not critical for me.