The course is a good continuation of the first one. Only criticism is that it uses an out of date version of tensorflow as the final assignment. It has a completely different syntax to modern tensorflow.

This was a great course....but at some places I felt that the details have been hided a little....only in few videos.........but overall it was a great course.....best of the courses...I have ever seen ..

This course helped in deepening knowledge about optimization techniques and how they could make ML/DL algorithms robust while training. This also provides a good introduction to the Tensor flow framework.

The lectures are all really good, but the programming assignments feel like they hold your hand too much. It's very easy to sort of slide through them without having a good understanding of the material.

Everything till now was good, But I can't tell why my forward propagation method is rejected although it matches the expected output. So my marks were deducted for it without any reasonable explanation.

A bit boring with this course at the first sight, but later when you work with the real system, this course can be a bible for you. The valuable experiences from Andrew Ng are shared through this course

Though very good, his course might be a little less polished than the previous. One more week diving into frameworks would (maybe keras to see a more functional level of Framework) would be appreciated.

There was no exercise on hyper-parameter tuning so the course seemed incomplete. I think week 3 should be split into 2 weeks with first week focusing on hyper-parameter tuning and second on TensorFlow.

Ng is an excellent teacher, and it was fun to learn about programming frameworks. However, the programming exercises are very simple, and the videos about numerics go very slow, thus 4 stars and not 5.

Very good course, useful and smart. Some of the example are on tensorflow 1 but I think that they will update them soon to keras tf2 Thank you!

I will pass on what I have learned here to undergrads :)

very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course

great and practical insight. carefully crafted assignments. still coding in python and the quirks coming with it are sometimes of equal difficulty if not worse than understanding the explained theory

Excellent content. The grader seriously needs to be updated thogh. For example, it needs to be Python2 and Tensorflow2 compatible and also needs to be robust in handling common syntaxes such as "-=".

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.