Math for Data Science — Lecture 01 Basic Matrix Operations via Python
In this lecture, we will study Matrix Operations (Initialization, Vector, Multiplication, Transpose, Special Matrix) via Python.
Matrix Definition
Matrix Notation
Matrix Initialization
import numpy as np
print(np.zeros((2,3)))
print(np.random.randint(low = 5,high=12, size=(2, 3)))
A = np.matrix([[1, 2], [3, 4], [5,6]])
print("Type of Matrix A:{}, having {} rows and {} columns".format(type(A),A.shape[0],A.shape[1]))
Vectors
- Row vector is having 1 row and N columns
- Column vector is having N rows and 1 column
Matrix Equality
Matrix Multiplication
In below Image 09, matrix multiplication is shown both via python code and hand as well.
- Matrix multiplication isn’t commutative (AB != BA)
Matrix Transpose
Rules of Matrix Transpose
Special Matrix
Reduce size of Sparse Matrix using CSR (Compressed Sparse Row)
Properties of Determinants
WORK IN PROGRESS !! Jupyter Notebook/.py will be uploaded soon..
Our Next lecture is about using Elementary row operations to find Row Echelon Form (REF), Reduced Row Echelon Form (RREF) and Rank of Matrix (https://medium.com/math-for-data-science/math-for-data-science-lecture-02-elementary-row-operations-via-python-46885a33a84c).
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