i had to fudge most of my submissions to make them fit the broken graders - and that was for those that actually had sufficient explanations in the material, and assuming the material was accurate. some areas are well explained, and its clear what's required, but others take huge leaps of expectation with little guidance, leaving the student to use trial and error to figure out what the expected solution is. that's very poor.

This lecture is very helpful and informative. One weak point is that there is little information on Tensorflow which makes the assignment unclear. What makes this worse is the assignment can waste you a lot of time (To be honest, my same code get different grades). And I cannot believe the team hasn't fixed any of them for over two years. There are a lot of discussions already. Coursera should really look into this.

Started this course with high expectations, coming from the previous 3 courses.Boring assignments, uninteresting topics (such as YOLO and neural style transfer), horrible video edits and Jupyter notebook issues ruined this course for me.The previous 3 courses were excellent, but this course needs more work. I wish there was more depth to the content, similar how the content were presented in the previous 3 courses.

The videos need editing. Ng repeats himself in several places as he tries to explain an idea. The programming assignments use too many global variables. The programming assignments real challenge seems to be in reshaping tensors when the reshaping is unnecessary. The wording of the problems in the quizzes needs improvement and clarification.

I liked the content. This course didn't feel polished like the others.

Too much focus on YOLO and other very computer-vision specific applications. The general introduction on ConvNets is good, but there are other applications than stuff for self-driving cars. I wish the examples were more diverse. In addition, the Jupyter notebooks used in this course are extremely unstable. You're unable to save your progress, and there will be problems submitting your coursework.

While I enjoyed Andrew's course on NN, I am a bit disappointed with his CNN section for one major reason: he did not explain the philosophy behind filters, etc. Instead, he tried to cover too many things based on the latest developments in the field of CNNs. Take this course if you don't mind being exposed to the subject without understanding deeply (no pun intended!).

The course itself is great, but the grader is seriously broken and the staff has not been willing to fix it for more than a month. So basically when you are finished with correct implementation, you spent lot of time in frustration trying to get through the grader, then you go to the forum and find out what need to be changed in your solution to pass the grader...

Course material is informative but when asking for help grading they just point you back to the discussion forums (i.e., no help). Having the output of the grader actually show what your output is would be helpful rather than just saying, "wrong, try again." This is especially true if your output in the assignment matches the practice example. Very frustrating.

The first two sessions are very well explained, with clear and precise examples. However the last two sessions, are explained in a very superficial way, without a good example, the explanation of these sessions are not deepened, the practical exercises don't teach how the problem is really solved. To truly learn, it is necessary to go out searching the internet.

The content is good, but the videos have not been edited properly. There are spoken notes to the editors left in the video and you can hear Andrew rephrase sections. To me that is really distracting, as I now start thinking about how Andrew phrases things, instead of about machine learning.

So a good beta. but certainly not a finished product.

The subject of the lectures are good. However, Andrew's voice is still relatively small with other noises.

Also, there are quite a lot of times when same words are repeated in the audio.

Finally , during the lecture a felt a little bit of sinocentrism from Andrew.

Multiple of the videos have editing issues and repeat clips. Programming excercises were good, but final programming exercise was a pain to finish, not cause of difficulty but cause of having to debug code without any proper feedback on whats wrong.

The lectures were taught far above the heads of my colleagues and I and the practical exercises were far too simple. I really wish the instructor took a course on pedagogy or went through Software Carpentry instructor training.

Week 4 videos were not edited at all. Week 4 lecture slides were not available for download. Week 4 programming exercise grader had significant errors such that the incorrect solution needed to be coded in order to pass.

Some of the topics were not explained in enough detail and felt like being quickly skipped. There were some problems with the grader system in one of the assignments which wasted a lot of time and caused frustration.

Content was great but a very buggy grader in week 4 made for a stressful experience that upset many students. Grader bugs caused me to repeat the course in another session and those bugs remained in the next session.

Programming assignments did not deal directly with the CNN models, but with auxiliary functions. Hacking the grader was more important than getting the right answer. Videos should have been better edited too.

Lectures are awesome, really inspiring and intuitive.Trouble with submitting assignments. I've solved the given question and resubmitted for almost six times, but the kernels showing up errors.

Horrible user experience with the "Jupyter Hub" constant issues that makes trying to do the exams an absolute nightmare and a perfect anxiety booster!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

In some tasks the delivery format is arbitrary and does not specify well what is wanted, that is, so the numerical value requested is good, the output is incorrect. due to format issues.

Not as good as the other courses in this specialization series. Specially, everything in week 4 has been rushed too much, with limited explanation of some of the most complex topics!

Programming part was not explained well. I guess programming syntax and flow of code should be explained too instead of just telling theory or focusing mainly on theory.

TOO MANY BUGS IN THE EXERCISES.It was a dis-incentive. Really.And I love me some deeplearning.ai! It has been great. The videos and content structure are fantastic.

Though the videos were very good but the assignments require too much from us and also there are few mistakes in week 3 and 4 notebooks which take a lot of time.

https://www.coursera.org/learn/convolutional-neural-networks/programming/IaknP/face-recognition-for-the-happy-house/discussions/threads/NcpP7i95EemJswr-eOHMNg