The materials about implmentation of transfer learning is helpfu, but again, I think the whole content of the first two courses could be compressed into one week. There're really not too much new things.

some clear instructions should be given for students. In exercise of week 4, there was no specific instruction about using .flow instead of .flow_from_directory, for labels we had to use to_catagorical.

Course assignments need a good overhaul. The two environments to practice the assignments (Jupyter workbooks and Google Colab) are not consistent, one throws an error while the other one works fine.

Very good course but please consider reordering the videos and reading especially in week 3. It is better to discuss the code in the video before moving to the notebook not the opposite.

Thank you

Course content was super nice.

But exercise organization is very annoying. not at all satisfied with the exercises. sometimes not loading and sometimes is really annoying . very disappointed

Good content for classification tasks. But didn't cover anything related to object recognition, localisation and semantic segmentation which are the challenging computer vision tasks.

The essential of convolutional neural networks is covered by this course although there ais unnecessary code in the examples and a lack of explanations especially in the assignments.

Too basic course. If its a practical course, then there should be exposure to more functionality of keras and not just the basic one which can be done from a blog/documentation.

Lawrence's lectures were good, but exercises were disconnected from course material. Having to do exercises in Google Colab and then redo in Jupyter notebook was inefficient.

Too easy. One can finish all exercises without learning much. The quality of explanations is poor. The whole course is but a short walk through Laurence's Jupiter notebooks.

the topic is interesting, and the course is quite hands-on, but the treatment of the subject is extremely basic. Videos are too short and somehow superficial and incomplete

cnn implementation theory should be covered better, giving more reason why the code is written this way, furthermore the last homework of the course was bad designed

very good course, but think it needs to go deeper in the functions and tools in tensorflow for conv netwroks, i have the feeling that the course is somehow shallow.

It was a great course, but in my opinion, it could have been even better if it involved more concepts & APIs to explore apart from the most in-use TensorFlow APIs.

Solid content, but it feels like it's not *very* much on top of the first course in this specialization. I think these two courses could be combined into one.

The course is good for beginners as it is very basic. It needs more advance topics like Detection using TensorFlow. Have a lot of scope for improvement.

There wasn't enough useful content. There were also many problems with the programming assignments, specially in the last week's assignment.

Google colab system for tasks is pretty bad, no control on the tasks plus it erases and u can't prove you did the work unless you save it

The course content is excellent. The talks with Andrew are inspiring, but the assignment graders are aweful and a big turn off.

This course is more of hands on activity in tensorflow. You need to have good understanding of CNN prior to doing this course.

Content is very limited.I wish they could have gone in-depth covered more areas of CNN like object detection ,segmentation etc

The course is taught excellently. However, there are overfull file stream operations in Python that the course does not cover.

Week 4 final assignment was quite bad, because there was new Python/Numpy stuff which was not covered at all in the course.

Please make the "Ungraded assignment" Graded and add more comments/directive in them to make sure we understand each steps.

Clear explanations. Good sample codes. Too easy. Doesn't go deep enough in terms of theory. Exercises should be mandatory.