This is great course for convolution neural networks . i really learnt a lot but the only thing which resist me to give five stars is i wanted to learn a more accurate face recognition system and to also be able to build an object detection model for my own projects through transfer learning i mean this should have been taught in the programming assignments.

Content was very good. You will get a good understanding about convolutional networks. Also good place to learn some basics of tensorflow and a little more about Keras. Some minuses: Home assignments where easy and you could do them without thinking much. Main frustration was due to having to reopen jupiter because it died very often. Hope they will fix this

The Course is exceptional in every detail in it. Andrew as usual explained the concepts in an intuitive way that sticks to the brain. The one thing that can be better is the part where R-CNN, Fast R-CNN, and Faster R-CNN were explained they did not get much attention and I feel like I have not grasped the idea behind them as the other parts of the course.

Excellent program with very valuable information regarding convolutional networks and intuitive exercises. I encourage the staff to update the tasks with the latest Tensorflow versions. Having the codes with Tensorflow 1. At least I would appreciate a clear information of how to migrate the codes from Tensorflow 1 to 2 (or whatever the latest version is).

There are some issues with the scoring of the programming assignments (I lost hours on the iou function and eventually realised I had to submit an incorrect formulation to pass, and the same thing happened on the triplet_loss function.) Other than these issues, which seem minor (and are) but cost me so much time and effort I would have given 5 out of 5.

The course as such is excellent, but quality of material is not on that level. In the videos Andrew is sometimes repeating himself (final cut of video is missing). Programming assignment for Face Recognition has two bugs, weights can only be loaded with a hack from forum thread and expected output of triplet_loss is not matching grader expected results.

The content is generally great and well worth it. I wish they would fix some of the errors, especially in ungraded exercises. You end up wasting a lot of time because of them. Perhaps the most frustrating aspect is navigating to the Jupyter notebooks. I wish the links to the notebooks were on the same pages as the Submission and Discussion links.

It is a nice course but one bad thing in this course is the programming exercise, sometime it is not very clear or there are some places that function has changed but the function mentioned is the old version and another suggestion is i hope there are more vids on the syntax on tensorflow, one vid in course 2 is not enough to handle the assignment.

I liked the course and the topics were discussed and learned. I gain much more algorithms and tools for various ML topics.

However, I am still missing the tools to start a problem from scratch - i.e. gathering data, arranging the data, building the proper data/structure for the algorithm. In the course, all the time, we get a well cooked datasets...

This was a great course: challenging and insightful. I liked the way in which TensorFlow was connected to published networks/papers. I did, however, feel that there was a significant change in effort required--compared to the earlier courses in the specialization. The increased effort was mostly due to the additional reading from the literature.

The lectures were outstanding for understanding the theory but the programming assignments had almost everything pre implemented. I understand that these algorithms require a very long implementation but that just made the programming assignment as just filling the blanks and didn't make me capable of writing code for these algorithms on my own.

I would very much like to give 5 stars, but because there are mistakes in the quizzes as well was in the assignments which are not corrected, I must decrease this rating by one star. I don't find it very appealing that I must find answers for quiz bugs from the discussion board.

So please, update the material whenever mistakes and bugs are found.

Course is amazing, teaches you a lot for ConvNets, image recognition, verification, building simple models in a couple of minutes, and refining them. The only drawback is that there are errors here and there, but fortunately they are being addressed, so future learners might experience less problems. Even with this, it was a really nice course.

Absolutely fantastic course,I just loved it.....Only problem was to me in the face recognition topic.

Training a siamese network need sharing base model with multiple inputs,which is important for training model with unconventional loss functions like triplet loss.And this isn't covered in the course.If that is included in it would be awesome

Again an excellent course. Great insights in convolutional networks. The programming exercises should use ore recent TensorFlow version as the functions cannot always be found anymore in the documentation, making the tf exercises hard to make - and at a certain moment it becomes a bit trial and error instead of a result of logical thinking.

Very informative and helpful to understand the fundamentals.

The exercises could have been designed better to understand TensorFlow, while one or two more exercises for the framework could have helped improved understanding of the framework. Also, several exercises feel like being spoon-fed and do not add much to the knowledge about CNNs.

Nice Course and it covers lot of details about the current concepts in deep learning. A little more details into YOLO and NST which included how we can train them ourself instead of using a pretrained model would have been better. A little more details about tensorflow and keras implementation of the algorithms could make it more helpful.

Overall a good course in understanding some of the concepts in the world of Detection of Images, Identifying them and building a fence around them to see what are their dimensions and how to ensure we dont fail to identify them and also identify humans which is becoming more and more prevalent in more and more countries across the world.

Week 3 of the course was a bit tough, well for two reasons. Firstly I thought the exercises had long explanations and too much detail which really needed much attention to retain the key information and be able to apply it. Secondly, I have been doing this non-stop for the last couple of weeks so at this stage i think I'm also exhausted.

Extremely interesting bit a bit too high level compared to the 3 previous ones. Convolutional Networks usage are well described but the technical implementation from scratch is incomplete which makes us dependent on tools such as tensorflow or keras. An additional video showing the maths behind the complete backprop would be a real plus.

Would have liked more explicit math, maybe as optional material, for some of the "you don't need to understand the math" parts. Also, there were some errors / inconsistencies in a couple lectures. (See the forums for more info.)Overall, though, a very good course - and much "meatier" than some of the ones that preceded it in this series.

The facial recognition and verification stuff was pretty cool and I'm glad it was included. I wish the python grader was implemented correctly and there were some technical difficulties with the Jupyter notebooks. The course opened later than expected which made me lose a month's worth of fees before a notice was finally sent out. :(

This course is amazing, but it just needs to be split up into a bigger number of weeks, have much more quizzes, like every 3 videos there's a quiz, but the problem is that you have to watch about 2 hours straight until you reach that quiz, it is hard to maintain your focus abilities in these circumstances. Overall rating? Amazing!

Awesome course ! I got to understand how some awesome applications like neural style transfer really work, implement the resnet 50 which is state of the art and most important I learned how to know which feature of an image an individual neuron in a given layer actually learns ! Thank you all for the effort, you did a great job !

Liked first three weeks but not the fourth one. There are couple of reasons why I didn't like much 1) car detection totally failed when i tested with my images. Images were clear and with proper angle (as if taken from dashboard camera). 2) face recognition assignment wasted lots of time due to incorrect data and expected result!