The Besttt.

informative

Very Useful

Quite nice.

Thank you!

Insightful

Some error

thank you

Very Good

good job

Decent

Thanks

Great

good

good

good

good

good

Good

good

Good

g

This is not an education in any way. Yes, Convolutional Neural Networks provides good overview of convolutional networks and technology behind it. I like the way Andrew Ng structured material and his way to explain some details. Unfortunately, as a common problem for all "Deep Learning Specialization", theoretical material only scratches the surface of the knowledge. There is nothing deep in terms of theory. You will have to spend quite a lot of time digging for information yourself if you plan to use course material for any practical task, or assignment. In order to get missing pieces, I got to go through whole Spring 2017 CS231n. It is fine if you have enough time to see two sets of videos, but I expected to get same quality of material here, on Coursera.

Another course issue is quizzes. I am puzzled what these quizzes are testing. Provided answers often assume tentatively more than one correct variant. Probability theory works against you - you may happen to select correct answers for some questions , but definitely, not all of them. In the same time, it is quite easy to derive correct variant from second try.

Course programming assignments are complete disaster. While I kind liked programming assignments from week 1 and 2, I felt like I wasted my time working on programming assignments from week 3 and 4. I expected programming assignment to guide me through some training of complex networks, give some practical insight, which I can use for real-life tasks, but it was not there.

There is a good introduction to TensorFlow, while Keras is not even touched. And many assignments of week 3 and 4 are using Keras. It is necessary to peek-up theory and practice regarding Keras elsewhere. After one get enough knowledge about Keras elsewhere - guess what - programming assignment becomes useless as education, because it is too trivial.

I really wanted to rate this course as Two-Stars, but video materials and programming assignments from week 1 and week 2 slightly improved my attitude.

While the online teaching of Prof. Ng, is excellent as in the other courses, this course specifically, has several pitfalls which can not be ignored:

1) The teaching and cover being given for TensorFlow are by far insufficient. If this subject is seen as an essential part of the course, it must be instructed systematically but this is not the case, unfortunately. More often than not, you find yourself doing guesswork in the assignments when it comes to TF code, which is also reflected in the Discussion Forum. So to summarize, TF must be covered in a systematic way, either in this course, or a previous one.

2) There is a bug on week 4 NST assignment, on the given code. Should be fixed.

3) There are several written correction to errors in OnLine videos. These Videos can and should be rerecorded.

4) Last but certainly not least: I have experienced frequent and really disturbing connection problems with the Python Notebook, with frequent connection errors, which can not be recovered and wherein one must open again the Notebook. While this was, to some extent, the case in other courses, in this course it was much more of a problem, especially in Week 4, probably due to a large amount of data, and where each rerun requires another 20 - 30 minutes. a MUST fix.

Thanks,

Y. Shachar.

This course let me down a bit. Like the other three in this sequence the content was great. Lectures were informative and I appreciate the detail that Andrew Ng goes into while talking about propagation. The pictures he draws are always instructive as well. It is not frequently you find instructors who are both experts in their field as well as know how to convey their knowledge to a broader audience.

Unfortunately, the production quality is not of the same standard as the previous courses. In the last three courses very occasionally would a sentence get repeated. Here it was, or seemed like, dozens of times. This can be very grating when listening to hours of lectures. Additionally, the homework grading system had a bug/error which resulted in lots of people being frustrated when trying to submit their work. While accidents happen the response of one moderators-"search this key word"-was not appreciated. I would certainly never tell my students to google something when I had made a mistake in the assignment. It was unhelpful, inappropriate given the mistake was on the creator's part, and borderline unprofessional. Then again, maybe the moderator was just English. I will finish the series but I sincerely hope the production quality is back to normal in the final RNN course.