Numpy Linear Algebra Cheatsheet

import numpy as np

# create a vector
x = np.array([1,2,3])
y = np.array([4,5,6])

# scalar operations
x2 = x * 2
x_plus_one = x + 1

# elementwise operations
p = x + y
q = x - y
r = x / y

# dot product - linear combination of the vectors
dp = np.dot(y,x)

# Hadamard product - elementwise multiplication
hp = x * y

import numpy as np

# create a matrix
X = np.array([
    [2,3,4],
    [1,2,3]
])

Y = np.array([
    [0,1,0],
    [1,0,0],
])

#see its shape
X.shape

# scalar operations
X * 2
X + 2

# elementwise operations
X + Y

# Transpose
X.T

# multiply
np.dot(X,Y)

# create an identity matrix:
np.eye(2) # 2 x 2

# invert a matrix
np.linalg.inv(X)