While the lab and videos explained the concepts really well, the codes from the labs are outdated. They are using tensorflow version 1, while tensorflow version 2 (current version) is very different. I have to go outside of this course to learn the new codes.

Other than that, every other aspect of the course is good. explanations are clear, videos and diagrams are very detail. Just the right amount of labs etc

The course is good in order to gain an understanding of the different types of Neural Networks, Algoritms etc. are present and what their purposes are. However, the course stems from 2019 and doesn't seem to be updated that much if at all. The mechanisms and coding required have change significantly since them, so the actual exercises are kind of outdated.

Lots of code and theory heavy, which is not a bad thing, but there is little thought given over to the actual learning objectives. There is also no real opportunity to practice learning to use TensorFlow. There are likely better tutorials out there, which is a shame because a lot of effort has gone into this course.

The Course was more about the the types of neural networks and how they work than Tensorflow, except for week 1 where we had a Tensorflow introduction, I could gather a lot from the programming assignments but I think there needs to be more about the Tensorflow library in the lectures.

I found the practice notebooks of this course to be lacking due to two reasons: 1) The notebook links are broken, resulting in my not being able to complete them. 2) The notebooks do not have practice sections where we could code ourselves following the examples given.

Pretty good content overall but the course does not actually teach you how to build deep learning models; it is lacking useful homework/programming assignments, provides extremely basic overview of the ML theory and simply walks you through a few worked examples.

Nice review when you have previous knowledge. The PyTorch course was more detailed. Nowadays, Pytorch is becoming more popular than TensorFlow and the new Keras API supports it (PyTorch) as well.

I think it is time for IBM to update its syllabus.

Good basic understand. No mix ups between general understanding and coding. Much better explanations than in the other courses of this specialization. Unfortunately content is still too shallow.

The course is good, but 1) the lab environment is not working at all.... I had to run the notebooks on google colab ! 2) The code is outdated. Tensorflow 2.x is out.

Overall good course but lectures were a bit weak on underlying math, compared to labs which made it a challenging at times to tie the two parts together.

One of the better courses in the IBM AI certificate. The notebooks are nicely annotated and have more relevant information than the video lectures.

All the code is TensorFlow1, which is unfortunately completely outdated. Also no assignments or final. But good for the fundamentals of TF.

Basic level but well explained, useful notebooks, not much on Tensorflow, more on the theory of the networks. Uses outdated Tensorflow v1

The course is good but you have to change the codes from TF1 to TF2 since is dificult for the learner tranaslate de codes by himself

I do not consider the practical part to be exactly beginner level, but the theoretical material is very good.

Labs would need to be updated to new versions of Tensorflow

The presentations were clear and concise

There is a sense to be outdated. Not much activity in the forum, code which is not updated...

It would be better if the exams are a bit more tough.

The questions are too easy to solve.

Too focus in coding but not in the underlying concepts and how to use the libraries.

Interesting view on tensor flow, but gap between labs and videos is quite big.

Low explanation.

But in this short duration we can't expect more.

Course needs to be updated to Tensorflow 2.0 at least.

we should have more videos and theory also..

Material excellent, cramed though.

The tensorflow version is outdated