The narration is poor. Instead of an expert lecturer, a narrator reads the text without understanding its meaning. Many fundamental terms are left unexplained.

Once again, great content and not that great documentation (printable cheatsheets, no slides, etc). Documentation is essential to review a course content in the future. Alas!

It is an excellent specialisation, except the pace of the speaker is very fast. It is difficult to understand, and it sounds very artificial.

Fantastic class but it takes WAY MORE TIME than what is reported, unless you just don't do the labs or casually read them high level. Going in-depth in the labs and doing the necessary work to understand all key concepts, and codes, will take you easily 3-4x more times depending on your current level of expertise. Example: a lab of 30 minutes has a length of 15 A4 pages when you print it. Now imagine all these pages contain key notions & codes. Superb class, but required time is highly underestimated (like most of the IBM Generative AI Engineering certification).

need assistance from humans, which seems lacking though a coach can give guidance but not to the extent of human touch.

Good content but I truly cannot understand...

Great course! Clear explanations, solid structure, and just the right mix of theory and hands-on content. Thanks to Dr. Joseph Santarcangelo, Fateme Akbari, and Kang Wang for making complex concepts so accessible. Really enjoyed it and learned a lot about transformers and GenAI!

I loved this course. It is very informative and has a lot of examples. It will take some time to master all this information.

Excelente curso IBM, una buena oportunidad para aprener sobre la IA

Get more familiar with transformer and its application in language

This course gives me a wide picture of what transformers can be.

Exceptional course and all the labs are industry related

honestly i learnt a lot

Nice Course

good.

Maybe a little chaotics. Slides should be available.

Some labs are outdated, the contents are rushed and the assessments are inadequate. Nevertheless, it provides a good-enough broad and general picture.

The course is interesting and challenging. The lab assignments should be divided into more parts. There's too much code to grasp in a single lab session, making it difficult to follow the task. A major drawback is the extremely long training time of the model in the lab work. For example, BERT took over an hour to train. During that time, it's easy to lose interest in continuing the course. Either the model needs to be simplified to train faster, or the performance of the environment running the Jupyter Notebook should be significantly improved.

The course is too machinery and jumps directly into deep topics without smooth introductions of the background or concepts. It is hard to follow the sequence of ideas.

Too much detail squeezed in a short time

Found this a very difficult course to understand. I do not recommend this at all, if you are not already a highly experienced professional in the field. Very badly explained, and impossible to keep up with. Way to much emphasize on technical lingo that to the untrained hear goes right over someones head. Continously had to google terms but did not help in making sense of it. Found very off putting in continuing course. Do not recommend at all.

It's one of the worst courses I've seen. I couldn't understand anything from their explanation and I had to resort to external resources to understand the topic (and I am already someone with ML background).

This course is soooo boring. It feels like it's written by robots for robots. I want to see humans teaching material and making it understandable, interesting, and relatable. This is just ai-slop.