Would have liked to see the math and more complete explanations for all the things that Prof. Ng glosses over by saying "you don't really need to understand XYZ". Even if this material was optional.

Could have increased assignments and some more indepth knowledge of tensorflow and proper installation way of tensorflow cause mine is showing error when iam trying to practice as shown in the video

I think this course is great. Because we learn about some definitions about hyperparameters, optimization which are frequently appears in papers or in the functions in some Deep Learning frameworks.

very good course with deep knowledge of each parameters. Little bit stretched at tensorflow. A bit of overview on tensorflow API and tensorflow architecture could have been better before exercises.

Great teaching from Andrew Ng as always. Would've loved to learn the math behind optimization techniques, but nevertheless Andrew gave intuitions of the algorithms which cleared up a lot of stuff.

few minor errata within the assignments that haven't appeared to be fixed even 1 year after reported. But otherwise learned a lot and enjoyed the course style and will continue to learn this spec

one remark for the last assigment, the neural network to read the hand signs, once finished and validated , I tried with my own pictures from my webcam, and it never worked (always predicting 2).

Having a good understanding of tuning the Hyperparameters is key to build powerful neural networks.

The course helped me to keep a focus on tuning and understanding the relationships parameters.

very quick moving but the assignments were too easy - they give you too much of the code (both the surrounding code which is fine but also the precise code for running optimisers for example.

This course helped me a lot to clear my confusion regarding various Machine learning jargon of words. It gave a intuitive understanding and helped solidify my foundation in Machine Learning.

I like this course a lot! 4 star due to the programming assignment. It is well designed, but hope the assignment could be more challenging instead of just giving us a taste of deep learning.

There were a couple of bits of maths that weren't fully explained and the very final programming assignement definitely had missing explanations but otherwise was really engaging and useful

The concepts were explained in a very understandable way. I would give it 5 stars if it treated the subjects in a deeper mathematical way and if the tensorflow version used was 2 and not 1.

The course is interesting but I am not sure that the best learning strategy is to fill in some lines within a program. I am disappointed that I can not download the material for future use.

With the basic knowledge I earned in course 1, it was very helpful attenting this coruse on improving Deep NN and I took a lot of notes during the course, to which can refer in the future.

Nice course for improving deep neural networks , they will show the all the paths available to improve a neural network , all you have to do is explore it based on your passion and need :)

The course content is great but the ending lacks tensorflow implementation of regularisation, hyperparameter tuning, learning rate decay, etc. and aslo still not confident enough in those.

The final programming task might contain minor bug, passed all sub-sections, but the final one result didn't match with the provided results, better provide more info for easier debugging.

Good material. The exercises are a little bit easy. The worst part is that after the last assignment, the certificate is done immediately and you don't have a chance to correct any errors.

Excellent lectures, well prepared, very good examples, great teacher.

I would happily give it 5 stars, if not the constant issues with Coursera infrastructure, crashing notebooks/kernels.

The course is very interesting and fairly well laid out but some simple typos can cause some confusion and they have been there for a long time based on some info in the discussion forums

Nice course. Great introduction to hyper parameters in neural networks and also nice assignment on tensorflow. It would have been even better if they introduced tensorflow in more detail!

Good material and some very nice practical tips. A few typos here and there in the course material made it difficult at times to debug the code, which is the reason for docking one star.

Good covering of many implementation aspects of neural networks. I find the practical exercises to lean on the tedious side while not bringing a tremendous amount of learning themselves.

It was somewhat frustrating to spend so much time coding raw python, just to discover that TF can do all of that with one-liners. Nevertheless it was valuable to learn the nitty-gritty.