The Blog

Understanding logistic regression mathematically—exploring likelihood functions, gradient calculations, and how optimization works.

Breaking down Gaussian Basis Regression step-by-step. Explaining the underlying math of transforming input data with Gaussian functions to coding solution using Python.

Introduction to the Lasso loss, and the math behind coordinate descent in the Lasso loss. Showing how the Lasso loss can be used to perform feature selection.

Follow up post on linear regression from scratch, this time implementing polynomial regression and seeing how it differs from linear regression.

Creating a linear regression model from scratch using Python with mean squared error and gradient descent.

Learn how to effectively use confusion matrices to evaluate the performance of models on unbalanced datasets.

A space for AI experiments, web development insights, and cloud technology discussions.