It turns out that scalars also share this coordinate invariance property.

事实证明，也具有这种坐不变性。

And what are vectors? Scalars? And what does this mean?

什么向？？这什么意思？

Remember how I said that linear algebra revolves around vector edition and scalar multiplication?

还记得我说过线性代数围绕着向编辑乘法吗？

The way you'll often hear this described is that linear transformations preserve the operations of vector edition and scalar multiplication.

您经常听到描述 线性变换保留了向编辑乘法操作。

The distance between you and a bench, and the volume and temperature of the beverage in your cup are all described by scalars.

你长凳之间距离，以及你杯子里饮料体积温度，来描述。

It's a little awkward to work with at 1st, because that left hand side represents matrix factor multiplication, but the right hand side here is scalar vector multiplication.

首先处理这个有点尴尬 因为左边表示矩阵因子乘法 但右边向乘法。

Our concept of the character is actually a composition of multiple Unicode scalar values, and decomposing these correctly is critical to getting that fidelity with Unicode, the characters, and the string API.

我们对于字符概念实际上由多个统一码值构成，正确地把它解构对于统一码精确性以及字符字符串 API 精确性至关重要。

You'll see in the following videos what I mean when I say that linear algebra topics tend to revolve around these two fundamental operations vector edition and scalar multiplication.

在接下来视频中 你会看到我说线性代数主题往往围绕着这两个基本运算向变换乘法。

But some special vectors do remain on their own span, meaning the effect that the matrix has on such a vector is just to stretch it or squish it like a scalar.

但一些特殊向仍然在它们自己张成空间上 这意味着矩阵对这样一个向作就拉伸它或者像挤压一样挤压它。

Well, the span of two vectors is basically a way of asking, what are all the possible vectors you can reach using only these two fundamental operations, vector edition and scalar multiplication?

好 两个向张成空间基本上一种问 你能得到所有可能向什么只这两个基本操作 向编辑乘法？