## Linear algebra in NumPy notes


```python
import numpy as np
import sympy as sym
```


```python
a11, a12, a21, a22, b11, b12, b21, b22 = sym.symbols('a11 a12 a21 a22 b11 b12 b21 b22')
A = sym.Matrix([[a11, a12], [a21, a22]])
B = sym.Matrix([[b11, b12], [b21, b22]])
print(A,B)

#x = np.array([[1,2,3,4]])
#y = np.array([[4,5,6,7]]).T
#print(x.shape,y.shape)
#print(np.dot(x,y))


```

    Matrix([[a11, a12], [a21, a22]]) Matrix([[b11, b12], [b21, b22]])



```python

```

e8a713d70ec645fa35bb493de5a89c5b0db807bb
ipynb
Jupyter Notebook
archive/ml-examples/linear_algebra.ipynb
ajrichards/bayesian-examples
fbd87c6f1613ea516408e9ebc3c9eff1248246e4
BSD-3-Clause
2016-01-27T08:51:23.000Z
2017-04-17T02:21:34.000Z
archive/ml-examples/linear_algebra.ipynb
ajrichards/notebook
fbd87c6f1613ea516408e9ebc3c9eff1248246e4
BSD-3-Clause
archive/ml-examples/linear_algebra.ipynb
ajrichards/notebook
fbd87c6f1613ea516408e9ebc3c9eff1248246e4
BSD-3-Clause
Qwen/Qwen-72B
1. YES
2. YES