This course should never have been published to coursera with such a state, it should have been a free YouTube video at best. What can be done better: - Make it a full course, i.e., start by introducing sequence modelling, why use attention mechanism over something like LSTM etc. - Start introducing concepts such as tokenization, vectorization, etc. - Describe transformer model in detail, take a simple 1 encoder / 1 decoder block and explain how it works with a toy example dataset. - Teach implementation of the toy example in code. - Introduce BERT with clear goal on what it aims to solve - Then comes the part that was discussed in the current course.

The videos do not properly explain how to setup Google Cloud account and help with running the lab. Since its just a couple of videos, I expected some solid lab work- but there are no efforts made by Google to help learners setup their lab environment and get the stuff running.

course was amazing gave me a good overview of BERT model and concepts like Encoding and decoding but not for beginner :>

The course's lab was highly interactive and effectively reinforced the material by providing hands-on experience with real-life datasets. The quiz was well-designed to test understanding and retention, making sure that key concepts were grasped thoroughly.

very clear and detailed explanation of the transformers with practical example of training BERT model

good

Excellent and concise presentation of Transformer and BERT models. The course designer may consider adding programming assignments to illustrate the concepts and to reinforce student learning.

I need to use my on GCP to run the lab. Otherwise, very good introduction to get going on Transformers

Personally, I don't think it comes under short course; it is like a superficial walkthrough short-video. It could have been a better short course. Maybe a better option will be to club all the introduction courses from Google Cloud together. Also, I wouldn't insist on a certification for this course.

Very quick, not very detailed; gives overall view if that's what its meant to do.

The first lecture was OK, but I don't think too much details about how the self-attention and FFN is constructed and why. The second part is just hard to follow without any instructions on how to get the exact setup, and, at the end, getting everything from TF hub is really just for illustration without too much to learn.

The introduction is too quick and shallow, with no other material offered to make up for it. Also, it forces you to use Google Cloud.

This course uses far to much jargon, without defining terms, and without providing simple examples that feature neural net architecture and matrix algebra. A complete waste of time.

The course has lot of bugs in interface. In addition there is no explanation on how to import code from github to google cloud.

I'm none the wiser.