Abstract: Neural networks that overlook the underlying causal relationships among observed variables pose significant risks in high-stakes decision-making contexts due to concerns about the robustness ...

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 ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Laboratorio de Análisis y Sustentabilidad Ambiental, Escuela de Estudios Superiores de Xalostoc, Universidad Autónoma del Estado de Morelos, Ayala, Morelos 62715, Mexico ...

The package contains a mixture of classic decoding methods and modern machine learning methods. For regression, we currently include: Wiener Filter, Wiener Cascade, Kalman Filter, Naive Bayes, Support ...

ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...