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(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

I have taken Machine Learning courses earlier from Andrew Ng via Coursera. I have always felt that the delivery of the material and the pedagogy are superb and have always rated a 5 star as also for the first course in this specialization. This second course had several interesting topics I had never learned in my previous NN courses at universities. The programming exercises for weeks 1 and 2 were excellent in helping recap the material in the videos and slides. However as far as TensorFlow is concerned, I was a bit disappointed because it seemed like we were muddling through the various code snippets rather than getting a firm grasp of what is obviously a very complex Deep Learning programming framework. But I understand the time limitations and I realize that this intro to TensorFlow is merely to whet one's appetite and encourage us to explore more about this framework as well as other frameworks. I believe it is up to each individual to explore the concepts further and get a better understanding.

The technology behind the courses is awesome as well as the programming assignment notebooks which were well documented and must have taken gargantuan amount of time and effort in prepping.

In summary, I learned a lot from this course and while my course objectives were not fulfilled, almost all of them were.

First for the worst: All discussion for this course has been moved out of coursera into Discord. I consider Discord as overloaded and confusing to navigate, especially in comparison to the neat and clear Coursera discussion groups. It requires an additional registration. Quite annoying for me, as I use Coursera mostly with the mobile app, where opening Discord involves an extra step and switching apps back and forth. No more support, help or assistance is given here on Coursera. Guys, please reconsider this.

It seems this course is a little out of it's creators focus. Corrections are still made, but on separate pages instead of editing the video, which would make it so much easier. The assignments are still maintained and updated regularly.

Apart from this, this course was great! Quality and explanations are excellent, many examples, (not too easy) exercises and assignments to practice what you learned. I would recommend it to someone with at least some contact with machine learnung and Python.

One more brilliant course by Professor Andrew Ng. It covered all important topics in model training and testing and hyperparameter tuning, ending with an introduction to TensorFlow. But even though Professor Ng stressed that the choice for a Deep Learning framework should consider the belief that the company supporting it has a long term commitment to keep it open sourced, he doesn't explain why he believes that Google will do so with TensorFlow. Even though I've been dealing with TF for a couple of years now, having complete both AI and Deep Learning Nanodegrees at Udacity, I still struggle to understand its cryptic innings. I hoped that Professor Ng could bridge that gap, but unfortunately that was not the case. Maybe in later versions of this course he can explain a little better how exactly TensorFlow works - we know it does, but it is very frustrating to depend upon tons of Stackoverflow queries to get syntax right and build even simple models. Thanks God he uses Keras in later modules.