Monthly Archives: May 2025

In normal arithmetic, the inverse of 4 is 0.25 because 4 * 0.25 = 1. If you have an n-by-n square matrix A, the inverse of A is an n-by-n matrix Ainv where A * Ainv = I, and Ainv … Continue reading →

I contributed technical content and opinions to an article titled “Understanding AI Large Language Model Transformers: an Analogy for Mere Mortals” on the May 2025 edition of the Pure AI web site. See https://pureai.com/articles/2025/05/01/understanding-ai-large-language-model-transformers-an-analogy-for-mere-mortals.aspx. The key technical component of large … Continue reading →

In mathematics, a condition number is a measure of how sensitive an object (array, matrix, etc.) is to some operation (dot product, matrix inverse, etc.) There are many different kinds of condition numbers. The condition number I come across most … Continue reading →

A few days ago, I was looking at linear support vector regression (linear SVR). Linear SVR is unusual in the sense that a model cannot be trained using standard techniques such as stochastic gradient descent or L-BFGS, because the loss/error … Continue reading →

I wrote an article titled “Linear Regression with Two-Way Interactions Using C#” in the May 2025 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2025/05/02/linear-regression-with-two-way-interactions-using-c.aspx. The most basic machine learning regression (predict a single numeric value) technique is called linear regression, … Continue reading →

In mathematics, a condition number is a measure of how sensitive an object (array, matrix, etc.) is to some operation (dot product, matrix inverse, etc.) There are many different kinds of condition numbers. The condition number I encounter most often … Continue reading →

If you have an n-by-n square matrix, the matrix has n eigenvectors (each with n elements) and n eigenvalues. The eigenvalue with the largest absolute value is called the dominant eigenvalue, and the associated eigenvector is called the dominant eigenvector. … Continue reading →