Thank you so much! I’m reading the document of numpy.dot you give.

Just wonder two things:

1.

The expression way in video as
--------------1
-------------[ - ]
--------------2
and
[1 2]
which is 1D and 2D? Currently I only sure [[1],[2]] is 2D.
For as I understand the
--------------1 3
-------------[ — ]
--------------2 4
means [[1,3],[2,4]]

2.

Is this “expression” or “drawing” way especially for ML area? Or this is common sense in math?

Put it this way. In Maths, we have dot product (aka inner product) and matrix multiplication. The former is for vectors whereas the latter matrices. np.dot may have a confusing name because it means both - not just inner product but also matrix multiplication depending on what are given to it as laid out in the use of it.

In the video, when we say dot, we identify them as vectors and not matrices. When we say matrix multiply, even it looks like a column vector, we would better term it as a column matrix. Afterall, a column vector and a column matric look identical when presenting in a maths equation in a video, don’t they?

After your reply, I re-watched the video carefully about the vector and metrics and finally got it!
Thank you so much!
Just want to check some details. In vector, is there a difference in
—1
–[ - ]
—2
than
[1 2]
?
Or is that just a different display way, not from column to row? (Because vectors have no concept of row and column unless put into 2D…“vector” right?)

It doesn’t care the orientation at all, but the thing here is, I am discussing vectors in the context of dot product. If you ask me, is there any other (non-matrix-involved) context when the orientation matters? That is the part I would suggest you to hold on until you get to ask a maths professional.