Previously, we looked at how data is represented and how matrix operations transform data. We talked about the structural components of attributes and explained that not all attributes make meaningful contributions to the process of learning of AI/ML algorithms. We mentioned solving a system of …

In the previous blogs we discussed data representation, transformation, the hidden structures of the transforming matrix and how it defines the solution of a system of linear equations. Matrix subspaces control what can be a possible output and identify the data redundancy. The objective of doing …

What did we learn till now? We understood how data is represented as vectors, why data transformation is a matrix multiplication, how to find out key attributes and remove data redundancy, different forms of solutions to the systems of linear equations, the role of four fundamental vector subspaces …