Good job by the instructor. But for a beginner to choose a particular dataset and performing image classification during preprocessing becomes a cumbersome task. CiFAR 10 dataset is already available in tf.keras's load_data module but using cv2 to read images and assigning label for any real world dataset would be more appreciable .

I understand that it is quite hard to explain how to code these scripts since the first task, it is not suitable for the Python and CNN beginner at all. However, the color code (red and green) is not explained deeper in more details, so that the output photos with colored texts are different from they should be.

For me the project was what I expected for a 1-hour project, but it would be great if it could be performed without the virtual machine, it's quite problematic and I couldn't type some symbols (= or []) with the common combinations of my keyboard. Besides this, I really enjoyed this mini project.

it was short n up to the mark, fully hands on and i came to know many new terms and their working. as it is a 1 hr assignment, this is just sufficient and satisfactory for beginners.

Perfect project for polishing you deep learning skills mainly CNN and getting introduced to using dataset available globally.

The project is good and the instructor explains the main areas of the code very well.

Course was good but rhyme interface was bad and needs an improvement

it is very useful for my career development. I like this very much

Not in depth, but still good for understanding

Thank you...this course too helpful to me

Not in much details, still worth a try.

Good Project for Biggener

so helpfull

Good

nice

EASY

e

Please, explain the structure of the data used and also the libraries used before starting the actual project.

If you do not know the theory about CNN its nothing at all cause code is found everywhere.

Very basic

Worst instructor till now on coursera, just useless

Very bad experience with rhyme console

i want to unenroll this

Worthless project