Very informative, could use some more room for practice.

Lab content must be updated with latest TensorFlow.

Thank you. thought it's could be more deeper

no audio in the last learning unit 5.

some questions are a bit confusing

Met expectations

It is ok

nice

good

This is a pretty good course on the different types of neural networks and their cousins. The presentation slides are really well done. The examples are programmed in TensorFlow. But the course does not really teach very much about TensorFlow itself. The opening lecture on TF describes it in terms that suggest this was created for TF 1.x, rather than the new structure in 2.x. But that turns out not to be an issue since they go into little detail on TF itself.

The programming examples are really good. However, most of the time, the CognitiveCourse.ai web site on which they run is usually not working. So you often cannot use the labs in conjunction with the lectures. You have to go back and access the labs sometime when the website is working.

While the lab and videos explained the concepts really well, the codes from the labs are outdated.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 The mechanisms and coding required have change significantly since them, so the actual exercises are kind of outdated.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.

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

very useful modules 1-6. however the course is significantly let down by the setup of peer marking in module 7

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