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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.

Pros: the course teaches CNNs clearly and concisely.

Cons: the memory issues on the last assignment wasted a lot of time.

Not solid enough and the exercise could be more organised. For example: some of the data downloading links didn't work.

It is too basic of a course. You should level up the difficulty and dive deeper into computer vision with TensorFlow.

Good content. The coding and assignments need significant improvement. There is no support whatsoever in assignments.

The assignments need a lot of work. They introduce obstacles not covered in the lectures and provide no explanation.

Expected a lot more in this course from the Tensorflow specialization. Something that'd take us beyond tf.keras.

Disappointing. This is more like Python course. Deep Learning specialization CNN course teaches you x100 more.

Could have dived more into the details and inner workings of Convolutional layers but overall awesome course.

complex ideas in very basic tasks that you can easily accomplish by copy-pasting from provided notebooks.

Lacks depth and complexity. The course is geared more towards complete newbies or high school graduates.

The programming tasks are not very helpful. The main difficulty is that unknown methods should be used.

Would like to have seen some information on techniques such as batch normalization and residual layers.

Not well-design, it is very light information and knowledge to learn, unlike the #1 introduction.

Final work had almost no connection to what you learned during the course.

Not enough hands on.

The knowledge taught in the course is over-simplified. The assignments are not well-designed.

The topics are explained very briefly.There is no in depth coverage of the mentioned topics

I dont think this course is very helpful in terms of price/how much you can learn.

Lectures are good but final exercises need to be updated and explained properly.

Course is good however there can be programming assignments for better practice.