The Tome of Numpy's Linear Algebra

A noob's guide

Numpy (which stands for Numerical Pytho) is a library of functions developed in 2005 by Travis Oliphant. This library implements the functions and tools to enable numerical computing and data structure manipulation. Its routine functions varies widely to handle the different mathematical principals required to analyze data; linear algebra, Fourier Transfor and matrices.

Linear algebra is the branch of mathematics concerning linear equations and their representations in vector spaces and through matrices. It was elaborated that linear equation is just a series of terms and mathematical operations where some terms are unknown.

In NumPy package, it contains the numpy.linalg module that contains all the fuctionalies required for linear algebra.

Prevalent in the field of engineering, and it is the basis of a number of mathematical tools; eg. Fourier series and computer graphic. It is considered to be the mathematics of data and is a surprisingly young field established ni 1800s.

In general, the numpy.linalg functions have been classified under the documentation to cover Matrix and vectors, Decompositions, Matrix eigenvalues, Norms and other numbers, Solving equations and inverting matrices as well as Exceptions. For this notebook, we'll be focusing on the Matrix and vector products. Some of the interesting functions we will observe are the followings:

• numpy.dot(a, b)
• numpy.vdot(a, b)
• numpy.inner(a, b)
• numpy.linalg.det(a)
• numpy.linalg.solve(a, b)

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks.

Let's begin by importing Numpy and listing out the functions covered in this notebook.