In lecture notes for cs231n while discussing checking analytical gradient with numerical gradient the paragraph says this:
Check only few dimensions. In practice the gradients can have sizes of million parameters. In these cases it is only practical to check some of the dimensions of the gradient and assume that the others are correct.
Be careful: One issue to be careful with is to make sure to gradient check a few dimensions for every separate parameter. In some applications, people combine the parameters into a single large parameter vector for convenience. In these cases, for example, the biases could only take up a tiny number of parameters from the whole vector, so it is important to not sample at random but to take this into account and check that all parameters receive the correct gradients.
I understand to check only a few dimensions but what does the part after be careful mean. I fail to understand that warning. can somebody explain it?