The videos and explanations by Laurence and Andrew are good, but I did not like the programming assignments in this course, because of their lack of explanation 'what to do'.

The programming assignments really need some fixing. They are not to difficult, but they lack explanation of what to do, which parameters to use, etc.

A good course, but again, not nearly as in depth as the original deeplearning.ai set of classes. The material feels introductory and at times superficial, with no real work required of the student to complete the class. At best a very early start to using convolutional networks with the keras apis in tensorflow.

I am giving only 3 stars because of two reasons: 1)the content is not significantly different than course 1. I didn't feel that I learned a lot more than course 1.

2)Assignment for week 4 is not well structured. Instructions are not clear. Moreover grader is poor quality and keeps running out of memory.

Not much recommended! Leave out too many details both theoretically and technically. The quizzes and the coding assignments are not well-designed. Specifically, the expressions in the quizzes are kind of sloppy and the coding sometimes requires tedious and repeated (no more than copy and paste) work.

I learnt a lot about CNNs and how to implement them, but I was taken aback to see advanced coding concepts being used in the programming assignments. I thought the concepts taught in the course itself were to be used only, but some parts of the assignments had parts which were too much to grasp well.

The course was very repetitive, not challenging, and therefore not particularly helpful. Andrew Ng's Deep Learning Specialization is vastly superior. Aside from getting used to TF and CoLab, I'm not sure what this helps with. I found it odd that it was recommended to me after the DL specialization.

Videos are great, but a little bit short. Comparing to AndrewNG courses and slides, the videos are merely the trailer for course. Grading is not what I would be expecting and it is one of worst I have seen on Coursera related to AI/ML. I was expecting a little bit more from this course.

This course is a really light introduction with CNNs in TensorFlow. While I enjoyed the videos, the content feels far too shallow. I completed the course in a couple days (and I'm not an expert in the field). It felt more like having gone through a TF tutorial than a grad-level MOOC

It is a good course for a fast overview on this topic. Be aware that it is not an introduction on ConvNN (but there are several courses of deeplearning.ai on this topic). If you are looking for a detailed course on Tf for ConvNN, I suggest you a book, the official documentation.

Basically a shallow introduction to programming simple CNNs with Keras. A lot is reused from the first course in the specialization. Reading one of the Tensorflow Tutorials/API documents on CNNs, Dropout, and TransferLearning will be time better spend, than doing this course.

The course itself is really good; however, homework problems at the end of the chapters are very unorganized. There is almost no guide at all. You may end up spending hours while trying to figure out why grader is having problems or your model's accuracy is very low.

Giving it 3 stars because of the last week's assignment. There is little to no information about the dataset and the learner is just expected to know how to deal with the data. No information on how many classes to expect as output and other necessary information.

What could improve it: Not enough depth in the practicals if you have already done Andrew Ng's course on Conv nets. No graded practical exercise.

What was good: Clear examples, Good setup to experiment with the algorithms & Speak explains concepts very clearly,

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.

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.

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.