Explore modern linear algebra concepts through hands-on Python programming in this comprehensive 2.5-hour tutorial. Dive into essential topics such as matrix operations, LU decomposition, inverse matrices, and null space calculations using the SymPy library. Master the Ordinary Least Squares method, implement the Gram-Schmidt process for QR decomposition, and understand the basics of Jacobian matrices and their application in neural networks. Gain practical skills by solving real-world problems and implementing algorithms, making linear algebra more accessible and applicable to data science and machine learning projects.
Overview
Syllabus
Linear algebra using sympy.
Matrices in python using sympy.
Matrix arithmetic using sympy.
LU decomposition of a matrix using sympy.
Inverse of a matrix.
Null space of a matrix using sympy.
Ordinary Least Squares Tutorial using Python.
Gram Schmidt process for QR decomposition using Python.
Basics of the Jacobian and its use in a neural network using Python.
Taught by
Dr Juan Klopper
