it would be ok if this course were free but it's not. there are technical mistakes in the multiple choice quizzes and the mandatory programming tests. some important topics are left unexplained while some are repeated unnecessarily. check the forums for more feedback.

Though it does give you a good introduction how to make neural networks, i felt that this course doesn't explain the Keras API library, which makes it difficult to remember and understand how things work.

I hope further courses in the specialization take care of that

I feel there are gaps in this course. But it was still worthwhile going through the material, to reinforce certain concepts. I had the impression that the course was initially classified for beginners, and that later on that classification was modified to advanced.

Rather basic, but the learner can strengthen it by broadening his reading. What I missed most is a conceptual introduction of what Tensorflow is and does.

I did not take the Deep Learning specialization before this one. Maybe that would have made a difference.

Personally, I feel like the entire series of courses for this specialization is missing the point a bit. I would have wished for a more deep dive into Tensorflow. It felt more like an introduction to deep learning courses than an explanation of Tensorflow.

Good introduction but lacks materials and practice to use after the class. Most of the materials its only referral. The trainer is good, except very shot videos and the setup on your pc are not discussed. But generally enoyed the lecture and learned a lot.

This course is simple in comparison with the Deep Learning Especialization, However you can learn new things like training from folders and using callback for early stopping. I consider the course is of, but the course use Keras and not tensorflow directly

The graded programming assignments are a little too ambiguous for beginners. They should really spend a day or two and model their programming assignments after the way that Andrew Ng did his programming assignments in the Deep Learning Specialization.

The homework is not designed well:1. The notebook is inconsistent with the colab env, and there are always problems here and there due to the inconsistency in tf version. 2. All 4 homeworks are similar, and it's simply a copy of the video lectures.

Not at the level of the courses from Andrew Ng. I expected it to be more about TensorFlow API than machine learning concepts. It tries to do both and does not excel in any. Got some stuff out it but you can probably say that by any course.

The course was comfortable to handle, explanations were were apt to the contents of the course, resources provided as reference were also really nice and apt, only felt that the course was a bit basic and well suited well for beginners.

A solid course, but if you have already take the deep learning specialization by Andrew Ng, there won't be too much new stuff for you. Callbacks and ImageDataGenerotor are new things for me, but I think they can be covered very quickly.

The programming assignments are vague and should be improved. The course lacks explanation content. It's more of a 'Don't question, Just follow along' tutorial. However, what is taught is presented neatly and clearly by the instructor.

I found both the videos and the exercises to be a bit too disjointed. I understand that you want to break things up into bite size, but these are a little too short and, for me, caused not appreciating hoe things flow and connect.

I understand that now Keras is inside TF 2.0 but the name of the specialisation is quite confusing. Here you learn how to use the high level API Keras not just the lower level TF. Really easy course, finished in less than a day.

Course Material not detailed enough and expected more from it. It does not contain enough variety in exercises and lacks a lot of concepts.

Anyone with good learning (and "overfitting") can complete 1 course in a day or two.

The material covered was very good but the options for each function should be covered. Also the labs and quizzes don't always work are the technical difficulties on submitting a lab are more work then the lab.

it was good but there is scope of providing more thereotical contents along with videos for the concepts beyond the scope of this course so one could get better familiar with the terms (e.g adam. cross entropy)

The course is very easy for anyone who has taken other deeplearning.ai courses. The course do has good and polished material but it is very small to be called as a course. Also there should be code based exams.

The theoretical concepts are too basic, the sample code to learn tensorflow are quite repetitive. I think the course should be much more dense both in DNN theory and in programming to worth the money.

A nice intro but very basic. Would have liked not using the keras api to have fewer things abstracted away. Then keras is a nice convenience once you fully understand what it is doing under the hood.

I felt the instructor is going too fast without covering the concepts of Neural networks, CNN and basics. Or maybe i need to take a supplement course for statistics and Neural networks by Andrew NG.

A very good introductory course, however the algorithms working under Tensorflow was omitted heavily. Some technical background on how these algorithms work would enhance the quality of the course.

content and teacher are great!

google collab and the submit process is tedious because of the hidden restrictions (memory space, computation power) and the bugs of the exercise submission tool

Oveall, a good course. Very good program examples. One area for improvement: fuker, more detailed explanations for ech line of code (both in the videos and the comment lines within the code.)