randomized-based-feedforward-neural-network 1.0.1

Package for feedforward neural network with randomized-based algorithms

Randomized-Based Feedforward Neural Network

Welcome to the Advanced Neural Network for Multi-Class Classification repository! This project implements a PyTorch-based neural network following the novel approach outlined in "A Multi-Class Classification Model with Parameterized Target Outputs for Randomized-Based Feedforward Neural Networks" (Applied Soft Computing, 2023). Our goal is to bring the advanced theoretical framework of parameterized target outputs into a practical, high-performance model that simplifies multi-class classification with enhanced separability and generalization. Through this implementation, we aim to bridge theory and practice, providing a resource for learning and experimentation with PyTorch.

Table of Contents

• Requirements
• Installation and Usage
• Contributors
• Contributing
• Acknowledgements

Requirements

• Python 3.X.X

Installation and Usage

Clone the repository and navigate into its directory:

git clone https://github.com/rorro6787/randomized-based-feedforward-neural-network.git
cd randomized-based-feedforward-neural-network

Install dependencies and run the training/testing script:

chmod +x setup.sh
./setup.sh

Contributors

• Emilio Rodrigo Carreira Villalta

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature-branch)
5. Create a new Pull Request

Acknowledgements

Inspired by various tutorials and resources on neural networks and my teacher's Francisco Fernández Navarro's article: "A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks:" Read the Article.