The course is good. Well explained.

The videos need some editing, sometimes speech is repeated which doesn't help with concentration.

Also, the forums need fixing, for someone used to Stack Overflow (and others) the forums work really bad.

Need more quick help from discussion forum since it is very important to understand the usage and working of components in the code. The course is superb in theoretical part but I felt I needed more assistance in programming exercises.

The contents are good, however the exercises includes too many errors and it takes too much time to read all the discussions to find out what the hack is. You should make sure the exercises are working smoothly before you publish them.

Since I have a computer vision background I was expecting much more challenges at this points when doing the pratical assignments. The explanations and intuititions about ConvNets are awesome , and this why I am giving 4 instead of 3.

programming skills by using tensorflow and Keras are required, and learned a lot of sophisticate program structure in this tough course. Worthy! Appreciated highly, but hopefully taking programming skills before starting this course.

really very good course with deep knowledge of deep learning backend but some extra content and work should to added to labs for elaborated explanation and practice.

some topics like how to select model model desining were missing.

Provide useful information in convolutional neural network and its application in image processing. However, there are many issues in the assignment and grading systems. Hope that these issues will be totally fixed in the future.

The grader in the final assignment was giving some problems even if the answer was as close as last decimal digit.Else the course was excellent ,it gave excellent insights to different architectures used in CNNS and its working.

Course content was really upto the mark with the current trends in convolutional neural networks, its the best choice for those with pre-gained knowledge about machine learning and have and idea of updating their skills in cnn.

As usual, the content is top quality. I did however notice several times in the recordings where Andrew tried to state something one way and then went back and restated the same thing - it seems like this should be edited out...

Really nice course of convnets. I think the intuition behind them could be more explained. In my opinion, the artistic part of the this nets should not be in this course as there is plenty of more important knowledge to adquire.

Thanks. Great course :)

There're some mistakes in assigments. Check the course forum when you're in trouble. The course forum is very helpful. Thank all the students and TAs who post their questions and solutions in course forum.

A very good course, but probably the weakest of those so far in the Deep Learning specialisation. Got better towards the end, but the first couple of weeks felt like some topics were not fully explained in terms of relevance.

Good course but I would really recommend doing a deeper explanation on backprop of CNN's. Also I think the explanation of YOLO is not arranged well. ultimately it gets there but when it is first introduced a LOT is left out.

Great material, but there was a bug in the grading of the final problem. To work around the bug in the grader, everyone is modifying their program so it doesn't match the expected results but nonetheless will pass the grader.

I think that you should give more details and instruction how to implement our own images, how to perform Transfer learning to the ConvNet for improving, how to train the network myself not only load the pre-train network...

Very Interesting

As usual, it is very very good from theory point of view. Practical examples are also really interesting.

Do not expect to be autonomous after the course, as you won't be able to use Tensorflow or Keras alone.

This course provides great introduction to CNN. It is an eye opener if you didn't know about CNN and it will take you to a level where you will be quite comfortable with CNN formulas and know a bit about how to implement it.

I learned a lot about CNN. Good programming assignments. Unfortunately the quality of this course was not as good as the three courses before (Grader Bug in last programming assignment, see discussion forums for workaround).

Вообще четко, еще бы отредактировать субтитры на английском. И цены не будет. Звезду снимаю только за то, что когда описывается модель с распознаванием и позиционированием начинается сумбур и надо по несколько раз смотреть.

Great lectures and exercises but the leave-behind study materials (lecture slides and notebooks) could be a little more helpful, and the frequent server disconnects have forced me to do most of the programming work offline.

A lot of useful information.

However, video editing is awkward sometimes plus Andrew's voice sometimes gets down to wisper which is hard to catch.

So good course but there is defenitely some spave to improve presentation.

Loved the content. Covers the fundamentals of ConvNets wonderfully. Need a lot more clarity on the use of TensorFlow and Keras in building these systems. Particularly the programming exercises felt a little obscure to me.

Although the course is comprehensive, at times, some of the concepts were skimmed over, especially in weeks 3 and 4. Topics like UNet architecture and Neural Style transfer felt rushed.

Overall, the course was excellent.

Great Course! I did the sequence model course before CNN. CNN programming exercises are challenging than the other four courses but could solve it with the support of discussion forums and hints provided in the exercise.