Alexis Alulema

Support Vector Machines (SVM) for Classification

The purpose of this document is to present the linear classification algorithm SVM. The development of this concept has been

(GitHub Repo: https://github.com/alulema/DDD-CleanArchitectureTemplate) These last 10 months I’ve been delighted working with ASP.NET Core, considering the improvements made by Microsoft

(GitHub Repo: https://github.com/alulema/SudokuSolverNet) I was revisiting a couple of basic AI concepts: Depth First Search and Constraint Propagation, and I

TensorFlow has several high-level libraries allowing us to reduce time modeling all with core code. TF Estimator makes it simple

In my two previous posts, we saw how we can perform Linear Regression using TensorFlow, but I’ve used Linear Least

Although Linear Least Squares Regression is simple and precise, it can be inefficient when matrices get very large. Cholesky decomposition

Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases

In my previous post, I mentioned 3 loss functions, which are mostly intended to be used in Regression models. This

Implementing Loss Functions is very important to machine learning algorithms because we can measure the error from the predicted outputs

Perceptron is a simple algorithm which, given an input vector x of m values (x1, x2, …, xm), outputs either