Don't feel too bad about it, there are far too many notations for the same concepts - completely dependent on the background of the author. This becomes especi

Don't feel too bad about it, there are far too many notations for the same concepts - completely dependent on the background of the author. This becomes especially noticeable in hodgepodge fields like Machine Learning / Deep Learning, where you essentially have scientists from all walks of life/sciences, trying to convey the same ideas, but using their own standards of notation. Check out this Math Overflow answer from none other than Terry Tao, on inner product notation: https://mathoverflow.net/questions/366070/what-are-the-benefits-of-writing-vector-inner-products-as-langle-u-v-rangle https://mathoverflow.net/questions/366070/what-are-the-benef... Point is - he listed 18 different notations, on the spot, all "belonging" to different fields. Now imagine 18 different scientist, from their respective fields, writing research papers using their preferred notation. Sure, there's going to be a lot of overlap - but there's still room for confusion, even for seasoned engineers and scientists. (I just used ML and DL as an example, because that's the field where I've noticed the most of this - because of the nature of those fields)