Very Good!

Just great!

Nice course

Good Course

Excellent

very good

Excellent

Excellent

wonderful

good one

great

great

super

good

good

Very good course which gives a good introduction to the field. Don't get intimidated by the math you will see and make sure you understand the workflow. Once you do that you will basically repeat it in which one of the neural network types presented at the course. In a negative not, I missed the intructor elaboring how to identity problems that could be approached by applying DL. But I complemented studies on other documents in the internet and that's ok.

It was a pretty good brief, rapid intro. I frankly was expecting more content on options and explanations, but it covered the very essential basics. The final exercise did ask for students to use tools not gone over in class (a bit of scikit-learn). Since I've used scikit-learn before, this wasn't hard for me, but it may be for a newcomer, and actually isn't needed to meet the goals of the assignment, so I'm not sure why it was there.

The course contents are not in-depth enough. The server for Jupyter notebook running is way too slow. Besides, the peer review homework is not that good, because some people didn't read through the questions carefully enough, and they misunderstood the questions themselves and could not give fair enough grades to peers. If the final assignment can be made to auto-grading one, it would be much better (we can set the same random seed)

I have experience of Deep Learning, so I am able to walk through this Lession quickly. The main focus is on Capstone Project, and I have learned something on it. To be honest, this lession is very elementary. I suggest to introduce more Deep Learning models and approachs in this lesson.

This is a great course. The lectures are boiled down to the essence of neural networks using Keras. I give four stars instead of five stars, because the IBM labs environment that the course uses was quite slow and buggy, so I ended up doing the exercises in Google Colab.

The Course is basic but interesting. I missed an exercise on backpropagation with the same explanatory level that it had for forward propagation. The last activity needs to be reviewed because it is confusing.

Nice overview. The coding exercises could be deeper, and in the second half of the course lose any depth at all. Understandable for such a short course, but still felt like a missed opportunity.

A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.

I took this course for understanding the TensorFlow properly. Now I am in the situation to understand all the frameworks. Thanks a lot for providing me this free course

Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras