As far as I know, the rank of a matrix is the dimension of the vector space generated by columns.
In NumPy notation,
x = np.array([[1, 2], [2, 4]]) has a rank of one.
np.linalg.matrix_rank(x) confirms that it is one.
While studying the TensorFlow page shown below, https://www.tensorflow.org/get_started/get_started I saw the following remarks:
A tensor's rank is its number of dimensions. Here are some examples of tensors: 3 # a rank 0 tensor; this is a scalar with shape  [1. ,2., 3.] # a rank 1 tensor; this is a vector with shape  [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3] [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
I'm totally confused.
Q1. What is the relationship between the rank of a Matrix and the rank of a Tensor? Is it a completely different thing?
Q2. In the case of the above matrix
x = np.array([[1, 2], [2, 4]]), is the rank two if we assume that it is a tensor not a matrix?