Really liked the course content but the true learning was in the homeworks that had the implementation details. After completing the course I was unable to download my own completed assignments as the course assignments were locked out for me. I don't want to re-submit any of them but I want to download my work to be able to refer to it and learn from it. I can see posts in the forum asking me to download them when the next session of the course becomes available, but I cannot afford to keep on paying ~ $50 subscription until it does.

The content covered is excellent as with the other courses.

However the material in this videos etc have many editing glitches. In addition some of the notebook based programming assignments are misleading and have minor errors that caused auto-grader issues.

In addition the programming assignments seem to be dumbing down. You spend lots of timing solving syntactic nuances of tensorflow, Keras etc rather than being asked to solve cerebral problems that help understanding of the concepts.

This covers hugely important information and really deserves five stars, but it is fundamentally clumsy. Even leaving aside the unprofessional disaster that is the week 4 assignment 2 grader, the difficulty level is all over the place and the description of the style transfer is borderline incomprehensible (possibly because Prof. Ng is trying to soft-pedal the linear algebra?)

Coursera, Prof. Ng, please take a second look at this one. It needs -- and deserves! -- better work.

Assignments really need to be improved. Of all the courses in this specialization, this particular course frustrated me a bit. Thanks to the discussion groups, I was able to sail through.

Moreover, Grader should provide the summary of error in our submission rather than just showing wrong submission. Course Material was really good. 5 on 5 for that part, but the assignments really troubled me and others as well, as can be easily seen in the discussion groups.

Lectures were great. The descriptions for all applied operations, algorithms, etc. by Andrew are excellent. However, the Programming Assignments this time around demanded a lot of looking up TensorFlow and Keras functions (even during the Keras Tutorial). Especially Week 3 was a struggle for me. At some point, the framework simplicity is turned into rather harsh complexity. A better explanation of what TensorFlow/Keras commands to would be of advantage.

First of all, thank you for providing such a rich content.

I know its hard to strike a balance between covering content and "actually" delivering them to the student. Course #3 and especially #4 felt very rushed when it came to the exercises. The tensorflow concepts that came back out of nowhere and solutions would have been nearly impossible without the copious hints.

PS: Course 4 "happy house" face recognition assignment was choke full of bugs.

Very good introduction to concepts on Convolution Networks. It would have been great to put more emphasis on how actual models like "FRmodel" are trained vs tested. E.g it would be great to provide information on the fact that 3 parallel networks need to be used that share weights. So more exposure to practical aspects of implementation would be useful. Essentially a lot more time can be spent on exercises than what is meant for them

Unlike other courses in this specialty, this course was primarily focused on describing some specific methods/approaches (which happened to be very popular) rather than describing high-level concepts. At some points, I had a feeling that the course material reads more like a journal club. While journal clubs can be very useful, I preferred more if this course was mostly focused on overall/generic concepts.

This was an interesting course. It provides a high level look at face recognition/verification and various state-of-the-art aspects of convolutional neural networks. The one thing I found frustrating in this course was the grader. It was very particular for at least one homework assignment on the order in which you entered your variables. I spent way too much time on debugging for simple things like that.

The content was great, and is probably the best available. However, the grader was so flaky it really shook my confidence in the material. I'm the type of person who will try and try until I'm literally about to give up before I look for help in the forums, so I lost a LOT of time on these exercises. This was by far the WORST of the five courses in the specialization. Sorry to yell, but YOU CAN DO BETTER!

The Keras and TensorFlow versions used in this course are by now to a large degree outdated. The Newest TF version is at the date of writing 2.3, while the course uses <2.0, so many of the functions used are deprecated in the newer versions

However, Andrew's explanations are great as always except for the convolutional implementation of sliding windows in the 3rd Week. (therefore only 3 stars this time)

Depplearning.AI: Please do not release content unless it is ready. The content is fine, but the assignments were clearly hastily put together and had basic bugs discussed all over in the forums. In particular, week 4 is a complete mess. Boiler-plate code that doesn't even relate student-content (to load a dataset) doesn't even run for many people. This wastes everyone's time. Really disappointing.

Overall a great intro to CNNs. But the last part of the course on object detection and facial recognition is very superficial. It explains the logistics of the disciplines (how to keep track of bounding boxes, etc), but it doesn't teach how to actually build such a system. The exercises make you fill in a bunch of indices and do vector math, but deliver the actual hard parts inside black boxes.

Very good covarage of the algorithms when it comes to analyzing pictures, and a good intro to the theory behind the models. But it is too little emphasis on other uses of convolutional networks like 1d convolutions, causal convolutions and similar. Maybe there are some coverage of these topics in the sequence course in the series, but it should be covered here to a larger extent either way.

All video content of this course where great, but i can't say it about programing assignments. YOLO and Neural Style transfer are by my opion advanced topics. I would more appreciate longer programming excersice, not only something where i only add some piece of code and i hardly understand what is going about. For example, convnets were clear, i could implement it by myself, but yolo no.

Course lectures and questions are very good. The programming assignments are also good questions wise, but the grading mechanism is quite annoying. We had to find out clumsy workarounds to get the correct grading, in one case, the code change needed for getting the correct grade did not result in the expected output. Coursera needs to change the method of grading programming assignments.

In this course the material/resources regarding Keras is highly insufficient. So far I have made little progress in the programming assignments related to Keras and I achieved this by figuring out stuff by googling which took a long time. If the instructors could extend the course with stuff related to Keras it would be great. At least there should be references to sufficient resources.

I can't believe the number of technical problems I've had with notebooks not saving my work on homework assignments. It's very frustrating. The content is good and I will continue with the course, but this is the first Coursera course I've had (actually, the whole series in this topic) where I have experienced the lost of work - I just lost about an hour on a homework assignment.

I am very annoyed with the evaluation of the notebooks. Not with the content itself, but with the support from instructors, which is non existent.

It took me two days and 10 re-submitions to solve a problem that was unrelated to the code, but to the behavior of the grader system. No one replied my cries for help in the discussions.

Very disappointed with the lack of support.

The content of the course is very good, as with all the Andrew Ng / deeplearning.ai material. However production standards seem to have slipped for this one. Repeated sections in video material and a final notebook exercise that contained errors and required finding a work around that was posted in a discussion forum. I hope these things can be corrected for others.

I have mixed feelings about this course in particular, although one learns many interesting and useful concepts, I did little implementation on my own. Also the involvement of Keras I found annoying, yes it eases the implementation of ConvNets, but while learning I would have preferred to use tensor flow instead, or even implement a simple NumPy ConvNet on my own.

The content is very good. The exercises are a bit useless. Don' expect to be able to use tensorflow after this course. Furthermore, they teach the syntax of tensorflow 1 rather than the new 2. Therefore, when you try to solve the exercises you don't understand the discrepancies between the online documentations and what they want for these exercises

Excellent course! I have learned the skills to combine image processing with machine learning.

However, the assignment of the Week 3 has a problem that you have not fixed for a long time, and thus it wasted some time. And the assignment in Week 4 has problems of submitting, and that's why I only rated three stars. Hope you can fix the problems soon.

First, there should be an upgrade to TF 2.0. In at least one instance the documentation for a function was non-existent. Second there are many places the videos can be cleaned up eg. transcriptions are just wrong like a machine did it, or the speaker repeats the same thing twice in rapid succession. Overall the course felt unpolished and dated.

The Face_recognition assignment was a tough one to solve, i only got grader problems but still i wasnt able to figure out how to pass grader even though my code yieded right answers. That's my only complaint otherwise the videos from Andrew were really easy to understand and the programming assignments were very well documented. Thanks :)