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
software developer & machine learning engineer
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