Eric Gutiérrez, 6th February 2026. A Python implementation of a 1-hidden layer neural network built entirely from first principles. This project avoids deep learning libraries (like TensorFlow or ...

Python Physics: Create a Linear Regression Function using VPython! 🐍📈 In this video, we’ll guide you through creating a simple linear regression function to analyze data, visualizing the results ...

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...

This project implements full-batch gradient descent (FBGD) for linear regression, comparing CPU serial and GPU implementations. The assignment demonstrates: assignment-5-linear-regression/ ├── ...

We will build a Regression Language Model (RLM), a model that predicts continuous numerical values directly from text sequences in this coding implementation. Instead of classifying or generating text ...

ABSTRACT: Blood transfusion involves the transfer of blood from donors to patients. A blood transfusion is carried out every day in the hospital. When blood transfusion is done correctly, lives are ...

Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

In this tutorial, we demonstrate how to use the UAgents framework to build a lightweight, event-driven AI agent architecture on top of Google’s Gemini API. We’ll start by applying nest_asyncio to ...

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...

ABSTRACT: This paper proposes a universal framework for constructing bivariate stochastic processes, going beyond the limitations of copulas and offering a potentially simpler alternative. The ...