This course gave a very good high-level overview of the material, with lots of real-world examples. The programming exercises don't require a lot of Python knowledge, so long as you can use online search tools effectively to get command syntax and other tips. The starter code may seem a bit like it is leading you by the nose to the solution. But, given the time frame of the course, it is excellent for older learners who probably have less time to dedicate to the coursework (due to work and family constraints). The English transcripts are not very good, at times, from what I noticed of the top of them, as Andrew was speaking. Even though the subject matter is usually considered intimidating, it is presented in a very manageable way, here.

Good content, worth the money even considering the not so "fair" subscription policy and switching sessions, but as it's for profit business, it is understandable. W4 face recognition assignment is very buggy, also the whole website seems to be really slow in general. What's even worse is that many people complained about it for months yet neither Coursera nor teaching stuff from Stanford did anything to fix it. Making unfinished product and not fixing the bugs in such long period of time doesn't reflect well on Prof. Andrew Ng, since after all he is co-founder of the platform. Regardless of that I'm still very grateful to him and the stuff from Stanford for the high quality of the material presented here and for so many great exercises.

The course is wonderful and explains almost every thing very eloquently.However, the course should mention Tensorflow and Keras as prerequisites for the course since they are heavily used in the assignments. Although there has been an effort to explain how tensorflow works, not enough details and not enough time was provided to get a good grasp of tensorflow and keras. So a beginner in tensorflow and keras will find it hard to stomach the essence of how to code these systems even if she completes the assignments with ease.A small course on just tensorflow and keras may be introduced in the specialization before this course. That would greatly increase the efficacy of this course and will also make the course more interesting.

Great Course!

The videos are very clear and insightful. The course doesn't provide a deep dive into the subject, but it will give you clarity and knowledge in the field of CNNs. The "homework" is very helpful and detailed I totally recommend follow through and do it!

The reasons for the 4 (and not 5) stars are -

1. The hands-on homework can be a bit unclear and convoluted sometimes - it may require some digging on the discord channel to find the issues you are facing with

2. Most of the topics discussed on the course are from ~5 years ago - I would love to see some update into the course I guess there were other breakthroughs in the field since it was created

But overall, the course is a great start into Deep learning and CNNs.

Course contents (5/5), -0.5/5 because I thought there are alot of information, and it needs a wrap up at the end of each weak, some information like how exactly the back propagation of Convolutional Neural Networks is performed was not clearly mentioned, even if it is not vital, having a bigger picture always works for me, the other -1/5 is for: I thought that this course's programming assigments are organized in the best way, sometimes giving more than necessary hints, and sometimes giving less than necessary, also the Kernel sometimes hangs and this wasted for me alot of time, after all I want to say thank you for the effort that you have done for the course specially Prof.Andrew, I really am learning alot thanks to you

A great course as expected. I give many credits to the creators of this course for all the contents and so clear teaching!

In terms of coursera, I was almost shocked when I tried everything but found that all my submissions were gone, just gone to nowhere! Looking at all "NONE" symbols and recalling many hours I spent to figure out the right way to make them work, I felt so frustrated. FYI I finished the course just half a month ago and I wanted to download my submission for me to review after my subscription is due the end of this month.

To avoid the tragedy happened to me, please remember to download your submission before you decide to submit! Hope no others will have to bear this any longer.

Definitely helps focus your interest.

I'm sure many people take this course and absolutely love it. I didn't have that experience. Don't get me wrong, it's thorough, taught by an expert who, by the time you get to this course you'll trust unconditionally, and there's plenty of guidance from people who've taken the course before. I'm sure you won't run into any problem that someone else hasn't had before. Anyway, I did get something valuable from the course. I learned I have absolutely no interest in computer vision / image recognition. That made it very difficult to persevere and finish 4 weeks of this stuff, but it is a valuable insight and it lets me continue to course 5.

The course is great for the price but it's showing it's age and there are NO official mentoring resources. For a paid course, this is not correct. I had some really frustrating problems that I was able to figure out after going through the forums, but it would be really great if there was this feeling that someone from the course team periodically checks the forums or keeps updating the FAQS or frequent problem threads.

Still, I think this is a great course and a bargain given the price. it cleared so many concepts related to conv nets, and it was one of the more difficult courses so far! I'm glad I stuck with it and was able to finish it on my own with help from forum posts etc.

Well, I think the course overall is great, though I already know about the first 2 weeks information. However, the assignment of lateral two weeks is not throughly delivered in my perspective. For example, I can hardly believe that I mastered the YOLO method just by implement the predicting part without have a really hands on how the weights is getting trained.

Another thing I found it unsatisfied will be in the last three assignment we all use transfer learning ,but there is no guide about how we can implement transfer learning.

Oh, the last assignment of face recognition have a serious bug inside, in order to get it pass I have to upload one wrong snippet....

Very good lectures, good selection of content and sensible delivery.

The quizes are prety good as well, varied, making sure one paid attention to the videos. Basic lecture slides are available as PDF as well.

The programming assignments are a bit dissapointing: the tasks allow very little creativity and are often trivial. The grading depends on some custom magic, instead of using some real-world framework like pytest.

I feel the code written as part of the course is very far away from what would be expected in real-life application. Some of that is ofc simplification for educational purposes, but I feel engineering best practices were sacrificed too much.

Good course again from Andrew Ng that really makes it easier to understand the concepts of convolutional neural networks. Andrew explains everything in a very explicit way, that really helps penetrating the mathematical notations that describe these methods. The only negative feedback I would say is that the solutions to the programming exercises are a little too easy because the solutions are spoon fed to you a little bit too much. I am sure that this is done in order to avoid people getting frustrated and quitting the course, however it is doing the learner a disservice since overcoming those frustrating problems often are what you really learn from.

I think this course is fantastic - given you come equipped with the right expectation and prerequisites. I'm new to python programming and this was definitely a step up compared to the previous courses in this certification. I have a solid background in all things matrices - so the endless discussions of dimensions did not really help me that much, but I can see how they would really help others. IMO at the end of this course, I'm not automatically equipped to build my own ConvNet, but I would definitely know where to start, and how to learn (read the right papers, get the right code off github, start from there...) if I wanted to set out to do so.

great course from andrew ng, though i would like the programming assignments to be a bit more <<hardcore>>. i mean to do the whole work from scratch and not just load some huge models, though i get it, to train those huge nets the students would need a lot of time waiting. Still some modules i got my hands on, in some utils.py files, were really interesting and i think it would be really educative to write some code on them too.As a suggestion i would say since python is used the videos should focus a bit moew on the libraries, at least on the really important stuff like import pandas.

overally, great course! i recommend it, without any doubt.!

The content is amazing and very informative. The content is clearly explained and I particularly liked the references for each paper in order to get a more thorough understanding of the models in the lecture.

On the other hand, the video editing needs some taking care of since professor Ng is often heard repeating the same sentence which is often confusing. Also the weekly programming exercises, excluding the first week, are far too easy and require mostly copying and pasting.

As a matter of personal preference I would also prefer if we used the same library for all assignments rather than having to alternate between Keras and Tensorflow

Thanks for putting together this great course. I just finished the course and my initial review of this is that it was more exhausting than the previous courses. The Course is well defined and helped me understand how CNN is used and what are some of the problems that can be addressed through CNN. However, there were moments during the exercise where I found applying the concepts challenging. I may have to relearn, apply and practise more on my own to get to really understand more of this. However, this course has given me a great start on how I can address some of the issues and how this part of deep learning is applied. Thank you.

It's an amazing topic and the course for the main part is well put together. Some of the videos haven't had the retakes removed so Andrew literally repeats himself. I thought my internet connection was playing up for a while. Would be nice if they could clean this up to not waste everyone's time. Some of the tutorials can be confusing/frustrating if they don't work first time. The style transfer one in particular, the instructions don't make that much sense. They say add 2 lines but 2 weren't required etc. Error messages are confusing, and rerunning the example is error prone but this is more Python's fault than this course.

Andrew has a very good video-lecture style.

The programming exercises can be a bit frustrating at times for the wrong reasons, but at this point the course has been available long enough that you should be able to find a thread in the Discussion forum that provides enough hints to resolve any issue you might encounter. Nonetheless I appreciate the effort that went into designing the programming assignments.

As others have noted the video editing is surprisingly poor, with brief clips that should have been cut scattered throughout, but ultimately it doesn't detract from one's ability to absorb the content, so not a huge deal.

The material presented in the class is very interesting and useful. The explanations are clear and the examples are good, especially teaching us to use transfer learning based on pretrained models. The programming was very helpful.

I couldn't give a 5 though, because I spent so much time on the programming assignments due to errors in the assignments, the grader and the hints. Additionally the mentors need to monitor the discussions in the week's section. Some students' comments were helpful, but others were wrong and completely off the rails. Some mentor feedback would really have helped all of us.

Absolutely great content - many important computer vision papers discussed in an approachable way that highlights differences in approach.

However, the Nov 2017 version of the course suffered from at least two grader errors: in once instance requiring literal following of instructions that would lead to an implementation that is faulty under real world conditions or a broader test set, in another instance deviating from the given instructions in a rather random if minor way. Hopefully these will be addressed in a later session - if not, be prepared for a bit of forum reading and trial and error to pass.

There were some problems to get the graded functions through the grader, although they were actually correct. Having the grading system a bit more flexible would save everybody's time.

I didn't understand all the operations we did in tensorflow in the neural style transfer programming assignment. How did we choose the arguments for the "assign" function calls? How did we tell tensorflow to minimize the total cost by modifying the input image, not the weights/biases of the model as it happened normally? Although the theory seemed to be clear, I didn't really get how we did it with tensorflow.

I think the assignments for this course could be structured to help students learn better. Specifically, I think there was not enough instructions on the difference between Keras backend and tensorflow. This was confusing at the beginning. Also, I think the codes snippets that are left as None sometimes help too much that there is not much thinking required, so I found that it becomes a lot harder to think what line of code is needed when there is not as much instructions. But all in all, I rate the assignments very highly and these are some of the ways that I think can improve the course.

Very informational course and the pace is reasonably challenging. Knowledge learned in this course are super practical and can be directly applicable in many areas. The Keras curriculum is especially welcoming. Do take note that course 2 and TensorFlow are sort of hard requirements to be successful in this course. I did course 1 and this course together, and it turned to be not the best choice.

Also, the risk of taking a new course is there were definitely several technical glitches which resulted in more troubleshooting than necessary. I believe this would be better with iterations.

Good introductory course for ConvNet and its trending applications such as object detection and facial recognition. Materials are presented to give students more of an intuition and process to carry out ConvNet applications rather than a rigorous mathematical understanding. Basic TensorFlow knowledge is highly recommended or one may face difficulties or confusion during assignments. I personally would like the course materials to have more depth, so to really nail in every step in building a ConvNet application (since as of now the content can be surface-deep and easy to forget).

The course instructor did a good job in presenting the basics of CNN and some of its applications in the domain of computer vision. The applications presented are 1) Image classification 2) Object detection and 3) Neural style transfer. As a researcher myself, what I most appreciate is that the instructor presented the topics to sufficient depths enabling the reader to appreciate the underlying theory and at the same time keeping it high-level. For those who would like to go more in depth, the relevant citations are presented in the lectures. Overall a very satisfying course.

Great content! Andrew really makes the concepts crystal clear. The lectures are very coherent and extremely organized, in terms of the actual contents. I've took MOOC in CNN before, and personally felt that Andrew's version is the best.

I took one star off for two reasons: 1) Sometimes Andrew would say half of the sentence and then start over -- it seems to me that those were to be taken off during editing of the video, but they are not -- hopefully they'd get fixed at some point; 2) It would be nice to upgrade the code to TF2, as TF1 has become less popular for a reason.