The course is really nice. But would be better if the convolutional layers were a bit more detailed. It was a bit difficult for me to understand all the parameters e.g: input/output filter size.

The course was fine sometimes I feel too easy. I would like to see more of the available options for the layers, such as padding, stride. filter size, mean average, batch normalization, etc...

Great course to learn newer aspects of TF. For me a great revision of ConvNets and a confidence builder. If there's one thing I'd fix, it would be the autograder and how often it crashes.

I think this was a good course but the standard of exercises and quizzes was too easy. More conceptual questions especially in quizzes would help in understanding the topic better

Overall good. Could have gone in bit more depth on how various hyper parameter tuning and regularization methods impact the model training. Provide some best practices tips .

the last exercise needed a big upload, made it imposible (for me) to do. This was a problem not related to the subject, should use data downloadable directly from internet.

The assignments are not challenging enough. The concepts are really well explained and for someone with no background in this area, this is a great learning asset

Very good course that teaches you basics of convolutions, augmentation, transfer learning. Thank you to Mr. Moroney and the Coursera team for making it available.

The course was good. The only problem was the last assignment where some of the functions went missing. It was difficult for a beginner to catch such glitch

The best course to do. Especially after the specialization course from Andrew. It is really helpful to code all the concepts you learned from Andrew course.

I think most much of the course conent was same as the first course, this course could have been a little more advanced. But overall a great place to start.

The contents are a bit light from my point of view. I get it is to be accessible for more people but math and explanations are in some cases important

The course content was quite good and overall understandable but the exercises and quizzes were quite easy, they could have been more challenging

The hand sign assignment need more explanation about using flow object from ImageDataGenerator since I just know only flow_from_directory object

Last assignment could have been explained better. Laurence does not talk about ImageDataProcessing.flow, despite this is the only solution

I guess one thing was not studied, the method .flow() which get the images generated by keras with the dataset labeled for the final test.

More detail videos or links,examples for important techniques like dropouts and for like multi class classification which may be optional

The course is very useful for practical purposes. But this course didn't cover some advanced topics such as object detection and GAN.

very practical courses on implementation of CNN in tensorflow. Suggest student also take the deeplearning series with this series.

Programming assignments could have been better. The programming assignments at the end of each week were not challenging enough.

This course offers more executable functions than actually helping you understand (in-depth) how neural network really works.

Instructors are really good, but in my opinion, this course should contain Object Detection and Object Segmentation topis.

Short, crisp and to-the-point discussions... Complex practice exercises designed as easy ones for learners. Well done !

Great introductory course to learn the application of TensorFlow with Keras in the field of Convolutional Neural Network.

Excelent course! The examples could be more elaborated, but this is a minor issue. Congratulations, and thanks a lot!