GitHub

Neural Network from Scratch

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 ...
IEEE

Causality-Informed Neural Networks for Regularized Learning in Regression Problems

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 ...
Hosted on MSN

Neural network Python from scratch with softmax

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 ...
Hosted on MSN

Python neural network for multiclass classification using softmax

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 ...
IEEE

Lightweight Neural Network With Load Condition Regression for Hybrid Leakage Fault Diagnosis of Hydraulic Cylinder

Abstract: With the continuous expansion of automation and unmanned demand in industry, hydraulic cylinders, as one of the most important industrial actuators, urgently need fault diagnosis solutions ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using pseudo-inverse training. Compared to other training techniques, such as stochastic gradient descent, ...
GitHub

A python package that includes many methods for decoding neural activity

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 ...
Scientific Research Publishing

Rahimloo, P. and Jafarian, A. (2016) Prediction of Diabetes by Using Artificial Neural Network, Logistic Regression Statistical Model and Combination of Them.

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 ...
The Hacker News

200+ Trojanized GitHub Repositories Found in Campaign Targeting Gamers and Developers

Cybersecurity researchers have uncovered a new campaign in which the threat actors have published more than 67 GitHub repositories that claim to offer Python-based hacking tools, but deliver ...
Scientific Research Publishing

Larasati, A., DeYong, C. and Slevitch, L. (2011) Comparing Neural Network and Ordinal Logistic Regression to Analyze Attitude Responses. Service Science, 3, 304-312.

ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...