Well, let's look at the confusion matrices!

好吧，我们就来看看这混淆矩阵模型！

You can't make visual effects without vectors and matrices.

没有向量和矩阵就无法制作视觉效果。

They were asked to complete as many matrices as they could in three minutes.

他们被要求在三分钟内完成尽可能的矩阵。

There are a lot of things that make diagonal matrices much nicer to work with.

有很西可以使对角矩阵更好地工作。

Does it matter what order we put the two matrices in when we multiply them?

矩阵相乘时，它们的先后顺序影响结果吗？

Each of our 32 squares has 4 matrices, so that’s 32 thousand, 7 hundred and 68 bits.

我们的 32 方块中的每一都有 4 矩阵，所以是 32,000，700 和 68 位。

So far, we've been storing individual numbers or letters into our arrays or matrices.

到目前为止，我们一直在将单数字或字到我们的数组或矩阵中。

They are giant, inscrutable matrices of floating point numbers that we nudge in the direction of better performance until they inexplicably start working.

它们是无法参透的大型浮点数矩阵，我们在朝着更好的性能试探着前进，直到它们莫名其妙地运行起来。

And still with unit lengths. You often think of these as the rotation matrices.

还是单位长度 你通常认为这些是旋转矩阵。

That composition of three matrices gives us the transformation matrix.

三矩阵的组合给出了变换矩阵。

Most immediately, in the next video, I'll be talking about multiplying two matrices together.

最直接的 在下视频中 我将讨论两矩阵相乘。

For instance, in the case of two by two matrices, rank two is the best that it can be.

例如 在2×2矩阵的情况下 秩2是最好的。

In the next video, I'll get into matrices in transforming space.

在下视频中 我将讨论变换空间中的矩阵。

They were given a sheet of paper with 20 matrices which each contained 12 numbers, two of which added up to ten (for example, 3.81 and 6.19).

每人持有一张印有20数阵的试卷，每数阵含12数字，两两相加为10（如3.81和6.19）。

E.G., here's a question Does it matter what order we put the two matrices in when we multiply?

例 这里有一问题 当我们把两矩阵相乘时 我们把它们按什么顺序排列有关系吗？

Next video, by popular request, will be a brief footnote about non square matrices.

下一视频 根据大家的要求 将是一关于非方阵的简短脚注。

This video is No Exception, describing the concepts of inverse matrices, column space, rank and, uh no, space through that lens.

这视频是《无一例外》，描述了逆矩阵，列空间，秩，还有，不，通过这镜头的空间。

I showed what linear transformations looked like and how to represent them using matrices.

我展示了什么是线性变换以及如何用矩阵表示它们。

Multiplying two matrices is also similar.

两矩阵相乘也是类似的。

Always remember that multiplying two matrices like this has the geometric meaning of applying one transformation than another.

永远记住 像这样的两矩阵相乘具有应用一种变换而不是另一种变换的几何意义。