Assignment for Week 3 is just a load of BS. Complete mess with no structured attempt to explain relations between suggested data-structures and built-in functions that use them. Whole fairly nice course is completely ruined by this one mindless pile of 'fill in random line of code to get the result' approach.

On the top of that - a final cherry on the pie was complete mess with Week 4 assignment on face recognition. Multiple bugs in the assignment code and grading, broken db's for the notebook and complete lack of support from Coursera. A shame. Weak and shame.

This course is an interesting review about techniques of image recognition based on neural networks. Unfortunately, it is not possible to achieve a deep understanding of these techniques during the time the course lasts. The practical activities are just filling lines in programs following the provided instructions and, sometimes, it is even possible to do it without understanding the rest of the code. The frequent disconnections between the notebook and the server slowed me down a lot and even made me lose all my work and start from scratch several times.

The first couple weeks laid a good foundation for understanding CNNs, but I did not understand the point of diving so deep in to Computer Vision, especially having a lengthy programming assignment devoted to an algorithm as complex and relatively niche as YOLO. There are several different architectures/applications of Deep Neural Nets conspicuously absent from this entire sequence, most notably GANs and AutoEncoders. I felt a good deal of frustration when implementing the programming assignments in the latter half of this course

Overall it's a very comprehensive course with a broad set of topics which I found insightfull. However, the programming assignments, in particular the Happy House, was done in a rush due to errors in the models and code provided. Part of the assignment couldn't be tested just for the lack of access to the model and evaluated its results after its grading. The forums were also crowded with many threads talking about similar issues. Hope you can improve this section in order to create a more solid course.

Very buggy, videos having problems (like repeating phrase), many errors in notebooks so that you spend more time trying to understand why grader doesn't work than on actual exercises...

The explications are either too simple or too sketched, so that you never really understand where difficulties are. The programming exercises are hard on the programming part and too easy on the math part, essentially what it is difficult is using tensorflow and keras with little or no explications.

Good course overall, but the week 3 assignment has a bug. There are discussion posts with 19K people saying they can't solve it, and the TA even says he can't fix the bug in the discussion.

I did the whole course, but couldn't finish because this bug in one problem made me fail the whole assignment. Coursera's help center did not care at all. This course is a robbery because you literally cannot pass it with this bug, yet I can't get a refund.

The "mentors" that are supposed to help answer questions in the forums are essentially useless. They rarely answer questions, and when they do, they often don't provide any useful information and answer a question that is not what the person asked. For one of the assignments, many of the necessary files were missing, making it impossible to complete. That said, Andrew Ng is a great explainer and the course content is generally excellent.

Content was solid, however too much to fit into 4-weeks. Had issues with technical errors on every single assignment. The last two weeks assignments exhibited both grader errors and work-loss errors - for both weeks (last 3 assignments) jupityr notebooks dumped significant amounts of work despite session saves, and submissions ran into 0 values for some assignments, that were later given full credit in later submission attempts.

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.