Very basic Intro.

Little bit easy

Week 4 is weak.

can be better

nice Content

Just good

too basic

Too easy

good

Easy

The content was pretty good, although I didn't feel like it added much that I didn't learn through deeplearning.ai's Deep Learning courses taught by Andrew Ng. There were a few interesting details specific to Tensorflow, but overall I felt the course was just information that could be found on Tensorflow's documentation--I did not feel guided through the process (but I realize that's more of a personal opinion).

The reason I am giving only 2 stars is that some of the homework assignments were poorly explained, leaving me confused about how to complete them (especially the very first assignment). Some of them were not updated, and other students had issues with this as well, because they wrote about it in the forum. Some of these issues raised in the forum were never addressed, but have just been sitting in the forum for months and even years. I think this is very unprofessional if you are going to take someone's money for a course. There should be more of an effort to update the assignments, clarify them--or, if you aren't going to offer guidance in the assignments themselves, then you should have someone actually responding to people's questions in the forum on a regular basis.

This is a useful introduction to the TensorFlow libraries and related tools for implementing basic neural networks.

The videos are clear and informative, but do not go into significant depth about what you're doing. They mostly just walk you through the code snippets. I found the material far too easy.

Some of the quiz questions are poorly worded so it's unclear how to answer correctly.

Some of the assignments are not well designed. They walk you through some details with so much hand holding that it doesn't feel like you're doing anything yourself, then neglect to mention tiny details (which are irrelevant to what you're learning) that must be just so for the fragile grader to actually work. The help forums are filled with common pitfalls and solutions, so it's not hard to work around these issues, but this feels like a waste of time. Why hasn't somebody gone back and update the assignments based on all this feedback from the students?

2.5 stars. While the Andrew Ng’s Deep Learning Specialization was a great introduction, this TF Developer Professional Certificate was not on par, and I’m afraid I persisted mainly for the not-very-good reason of getting the certificate...Not a complete waste of time, however, as it does provide extra exposure to tf, though almost exclusively through the keras Sequential api. To pass, you need merely pass the quizzes, and those are really poorly structured. There are no graded coding assignments. There does not seem to be must mentor-activity in the forums, and a number of people have noticed possible coding errors and inconsistencies which remain unresolved.

Added later: I really recommend having a look at Coursera's "Tensorflow 2 for Deep Learning" specialization, which is offered by Kevin Webster at Imperial College. In my opinion, this course has a much, much higher standard.

I took the Deep Learning Specialization before (which admittedly set a very high standard) and expected this specialization to be similar, just more focused on Tensorflow. I couldn't have been more disappointed. Explanations are very shallow, and I totally missed the well-thought-out programming exercises. Here there are just notebooks with some missing parts that you have to fill in, without any explanation. You just copy over the code from the lectures, and it works, and you have not learned anything. A sneak peek at the next course shows that this seems to be organized in the same way. Luckily I finished this course within the 7-days trial...

I can't believe I paid $49 for this and knocked it out in <2 days. I now know how to copy-paste lines of tensorflow to do some very specific things. It's fine to not go in-depth on the math and everything behind the scenes if you instead focus on practical application, but this course does neither.

For a glorified tensorflow/keras tutorial, I would hope that after the course I would be able to build my own models, but I honestly know how to do nothing outside of the 6 lines of code I copied from the videos, and I don't even know how those lines work.

Disappointing course. It is an introduction to high-level Keras API and better be called this way. For anything else it is not useful. An introduction to AI, ML - really? :) It does not explain how do neural networks actually work and what is going on when fit() is called, etc. How about about gradients, optimizers, activations, etc? Only convolutional networks are properly explained. Labs are copy-paste from slides and zero thinking. I would better not comment on the quizzes.

The course does not goes deep (not even close) into explanation, and many topics/methods are just mention. Per se, that is not bad if you could use the forum to get the answers of the questions that arises. However, that is not the case for this course, where most of the questions are unanswered.

Moreover, there are many bugs/typos/mistakes in the given code. And because the staff does not answer the forum's questions, you are not sure if they are really bugs or not.

Very very very superficial course, both on the theoretical part and on the practical part.

The theoretical part is almost none. Each week you will have like 2 minute video explaining the NN that you will use.

The practical part is also very superficial because the only thing that you do is calling functions from Keras.

Don't recommend this course to people that already have some (very little) experience with tensorflow/keras. Recomend it to newcommers

Jupyter notebooks and video explanations have many issues (for example: https://github.com/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%204%20-%20Lesson%202%20-%20Notebook.ipynb - where fundamental terms like "accuracy" and "loss" are mixed up). Considering that many people who will be taking this course may be beginners, such mix-up can really affect what they learn and how they approach Deep Learning.

Many in the course were just taught superficially. No in depth clarification of things was given according to me. Rather I would recommend to check out Imperial College London's course "Getting started with Tensorflow 2" course which is awesome. There are 2 courses in the specialization, I have mentioned first one of them check out other(s) on your own. Happy learning.

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Course is interesting.

But programming assignments are not described enough. Leaving students with difficulties to understand exactly what is expected to get the grades. Even with a working solution, multiple assignments must be send to try to "fine-tune" the programming assignments to get a grading without knowing why it does not work.

It should not be called like it is called. This is not even close being "an Artificial Intelligence, Machine Learning, and Deep Learning". At most this is like "10 minutes into TensorFlow". You have to be at the absolute beginning to get out something from this course. Better read the docs and take A. Karpatny course on youtube

Honestly, this course doesn't meet my expectations. The instructor's way of explaining the code was okay .This was really a very easy course. The topics covered in this course can be studied from any youtube channel with free of cost. If you are wishing to get a deeper understanding of tensorflow then don't buy this course.

The lections were good enough, but the assignments are awful. In the first week, they say we can use TF 2.0, the Google Colab uses TF 2.3, the grading script accept only TF 1.14. I spent a lot of time only to figure out which version syntaxis should be used in order to pass the grader. That was awful.

Not going to lie, I'm pretty disappointed after finishing Andrew Ng's course and coming here for the next step. I feel like maybe Andrew raised my standards too high, but this course was extremely not begineer-friendly as an intro course, and I don't think I was able to pick up anything worthwhile.