We call the matrix full rank.

我们称这个矩阵为满秩。

For a full rank transformation, the only vector that lands at the origin is the zero vector itself.

对于一个全秩变换 唯一落在原点的向量是零向量本身。

But for matrices that aren't full rank, which squash to a smaller dimension, you can have a whole bunch of vectors land on zero.

但对于不是全秩的矩阵 它被压缩到更小的维数 你可以有一大堆向量落在0上。

But the matrix is still full rank, since the number of dimensions in this column space is the same as the number of dimensions of the input space.

但是这个矩阵仍然是满秩的 因为这个列空间的维数和输入空间的维数是一样的。