Good course, but the notebooks need a deep review to fix the problems related to balance between the requirements of the exercise and the resources available also a better explanation of the exercise aims would be a nice to have to avoid misunderstandings

Actually a great course. Only not getting more stars due to the issue encountered with the last exercise where there is an issue in loading the data files. The workbook keeps on crashing and there is no solution provided to resolve that.

The material is good, but there is not much thereof.

The duration of the assignmentsis greatly exaggerated, since most of the lengths for the readings and exercises are wrong.

The course can easily be done in 25% of the official time.

If you have taken the first course of the specialization this class was repetitive at some points. I also did not like that there have not been graded coding problems. I still got some practice and learned some new techniques.

Great course and great explanations. The version of TF on CoLab, however has missing information that do not allow to complete the assignments correctly unless you search for answers on the internet for error messages.

Not too much explanation. Very short course, just like Tensorflow tutorials in short videos. It should be covering all aspects in detail like Deep Learning Specializaion making person fully expert in applying CNN.

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

Boring. If you really want to teach Keras, please show how to write custom training loops. The train_step() function. Model.fit() gives an illusion of accomplishment.

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