This course explains the details of CNNs with a great simplicity. It also presents some state of the art CNN architectures with their ideas very clearly. Finally the assignments allow to implement several CNNs and also show how transfer learning is used to perform face recognition and neural style transfer.

One of the most important courses in the Deep Learning Specialization in my opinion. Good content, enjoyed the homework, lots of details for beginners and extra resources for more advance content. Would definitely recommend for anyone interested in working in Machine Learning especially in Computer Vision.

Exceptional Course. Learnt a lot from it. Takes a different approach to teaching than other courses in the sense that more focus is on applications rather than training of models for which a GPU cluster is a must. Thanks Andrew Ng and his team for the wonderful course. Looking forward to sequence models :)

This is the course I enjoyed the most among the Deep Learning Specialization Course threads. Seems very practical to me and I learned a lot about CNN. A few more detailed practice in notebook problems could've made things more interesting. Many thanks to Andrew Ng for making such wonderful lecture videos.

An amazing course to get an advance knowlege and practise "Convolutional Neural Networks". Andrew Sir makes the math and concepts behind the scenes very easy to understand. The course is easy to follow as it gradually moves from the basics to more advanced topics, building gradually.

Highly recommended.

Professor Andrew is really knowledgeable. The lecture videos he makes are really helpful for me. I really learn a lot from them. Also, the recommended learning materials such as academic paper he recommend are really useful for me if I want to further my learning on the residual network or YOLO algorithm.

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.

Exceptional course taking you into the real world of deep learning by exploring new concepts and classical architectures like LeNet-5, AlexNet, VGG-16, ResNet, R-CNN, YOLO, FaceNet and Style Transfer that propelled deep learning in new heights. Loved every part of it (minus some hiccups with the grader).

The course is just awesome both in terms of content that is being taught in the lectures and the assignments. Though, I think the last week is not that much important for the industry purpose but definitely it is good for those who are interested in non-industrial use of tensor flow and neural networks.

it's a wonderful course to learn CNN. thanks to the Prof. Ng for his excellent teaching. the programming assignment is clearly explained and structured. it is easy for student to follow and understand what they are doing. I am really enjoyed the learning. again, thank you very much Prof. Andrew Ng !!!

I finally understand YOLO! This course has the best material available on CNNs. Even though I come from a MSc in Computer Vision and Machine Learning, we didn't have enough time to fully cover 'complex' architectures such as YOLO. Thanks to this course I feel more up to date in the Deep Learning field.

Once more Andrew steps up as a brilliant teacher. I'm a biologist looking to improve my data science skills to better tackle medical imaging problems. I'm confident to say Andrew is the reason I'm going to make a difference in low resource communities in the future. Thank you, Andrew, you are awesome.

I really enjoyed this course. I found it pretty approachable. FWIW, I'd taken Andrew's original ML class, but then skipped 1,2, and 3 of the new one (and jumped into 4) The course really holds your hand, so be prepared to force yourself to try some of this on your own to be sure you've understood it.

This course is intended for ML learners who have background knowledge of NNs and want to enhance their scope of knowledge in CNNs. Prof. Andrew has been an amazing instructor. The material used in this course is mostly based on Tensorflow, so make sure to have a bit of prior knowledge in Tensorflow.

I have learnt a lot new things in this course, constructing exciting image/object detection projects with Tensorflow, Keras and even plain Numpy. Also, Andrew well explains many complex network architectures which illustrate various perspectives of the applications of convolutional neural networks.

This course really gets you started working with CNN. The only downside are the "bugs" in the assignments. My advise is to read the discussion forums before you do the assignment to know if there is a bug that you should know of before submitting.

Beside this minor bug, the course content is 5 star.

I am loving the journey I have started with DeepLearning.AI .Thank you to the amazing Teacher Andrew Ng. Sir for offering such wonderful online course which is affordable to anyone anywhere in this world. Thank you so much and I am looking forward to finish the last course of this specialization.

I liked everything about this course ! The instructor was very good at explaining the complex concepts and I really liked the quizzes and programming assignments , they really help brush up the concepts taught in the respective week !!! I would like to thank Andrew Ng for this amazing course !!!

Really enjoyed learning more about the current state of the art of image recognition models. Although the structure needed can be at times overwhelming, the concepts are clear and implementation via open source packages make it feasible. Many thanks for making this available, keep the good work!

Somehow, a bit harder than rest of the courses for me. I had problems with tracking dimensionality and tensorflow notebooks were hard and difficult to debug. I think it would be nice if tensorflow has its own as a course or 2 weeks maybe. But anyway the concepts explanations is great as always!

Excelent course. ConvNets are an eye-opening subject and the course explains the main concepts and applications in a simple way, indicating the source papers to understand better. I'd only ask for a couple of videos explaining in more detail backpropagation and the upload of the missing slides.

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

great ! It is complex though, don't get fooled by the doable exercises - to really understand you can take several loops.

Imagine someone breaks up recent complex research paper into python notebooks for you and you get this delivered like a delicious food - this is how I feel about this class.

This is an excellent course. Although I've got 100%, there are still some details and intuitions need to be figured out. Maybe I will go over it again. And of cause, I'm looking forward for the fifth course. I wish the fifth is not the last course. We still need to know reinforcement learning.

Very informative lectures with simple explanations of what the algorithms are doing. The programming assignments are extremely detailed and well explained. This makes it very efficient and fun to learn the concepts of Conv Nets, Res Nets, the YOLO algorithm and so on in a short period of time.