I expected some more explaination for the concepts. However from tensorflow website, more could be learnt.

The coding part was hard to understand. If that part could also be covered in videos as a tutorial.

Great introduction to unsupervised learning. However its an easy course with not much to offer

challenging but i wish the quiz questions were more useful in testing our understanding.

Missing more theory in the course, some labs is not working well. Need review all labs.

It would be grate that the examples have been updated to the TF 2.0 version.

i loved it. I have an undertsanding of different deep learning models

Was a really fun course, but the final assignments were very lengthy.

Good material but almost all the labs are too slow to run properly

Good introduction to Deep Learning Models with Tensorflow

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 course had some distinctly terrible aspects, for instance, introducing a topic without any form of background. Ie. "Transformers: here's a transformer". Before moving on to other subjects. It's optimized for time, and not for learning or subject matter. At times, it seems cobbled together from other courses. The producers couldn't be bothered to properly format, use proper style, or even at times make code examples properly functional in both the the slides and labs. Was this course created by professional software engineers? The course has no rigor - it does not prevent you from skimming through and simply completing each module. You may pass this course by doing no work whatsoever. It is entirely up to you what you get out of this course. That said - if you do complete the course by watching each video twice, taking notes, and doing external projects outside of the class, you can actually learn something.

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.

Overall the content is good, it's just such a pity the labs are sloppily, and not well put together. I found many mistakes going through them all. Some are easy 'copy-paste' errors, others are more fundamental. I think the authors need to do a full review of the labs and update them as required.