Preparing data for tensorflow is often easiest when done as a two step process. In machine learning, you often get into trying to plot points, calculate tangents, and a lot of basic algebra. Working out equations kinda’ reminds me of being in in-school suspension in high school. Except now we’re writing code to solve the problems rather than solving them ourselves.

I never liked solving for a matrix… But NumPy is a great little framework to import that does a lot of N-dimensional array work. A few basic tasks in the following script includes a number of functions across norms, matrix products, vector products, decompose, and eigenvalues. Remove/comment what you don’t need:

```
import numpy as np
from numpy import linalg as LA
array = [[-1,4,2],[1,-1,3]]
array2 = [[6,2,1],[7,1,-3]]
array = np.asarray(array)
converted = np.fliplr(array)
#ifsquare >> cholesky = LA.cholesky(array)
#ifsquare >> inv = LA.inv(array)
#ifsquare >> determinant = LA.det(array)
#ifsquare >> signlog = LA.slogdet(array)
print
print 'ONE ARRAY'
print 'Sum: ', np.trace(converted)
print 'Elements: ', np.diagonal(converted)
print 'Solved: ', LA.norm(array)
print 'qr factorization: '
print np.linalg.qr(array)
print 'Hermitian: '
print LA.svd(array)
print
print 'TWO ARRAYS'
print 'vdot: ',np.vdot(array,array2)
print 'Inner: '
print np.inner(array,array2)
print 'Outer: '
print np.outer(array,array2)
print 'Tensor dot product: ',np.tensordot(array,array2)
print 'Kronecker product: '
print np.kron(array,array2)
```

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