# MATLAB Multi-Layer Perceptron (MLP) Toolbox

Author: Marino Massimo Costantini
Last update: 05/12/2023

## Overview

This toolbox provides a comprehensive toolchain for creating, training, and deploying MLPs. The toolchain seamlessly interfaces with Python in automatic mode while offering a user-friendly interface entirely developed in MATLAB. This approach aims to simplify user operations while maintaining high performance, leveraging Python's faster execution in certain scenarios. The following files are crucial for optimal utilization of this toolbox, and it is recommended to follow the specified process. Users assume responsibility for their actions.

## Folder Structure

Currently, you are in the main folder named "MLP_toolbox." Within this folder, you'll find three subfolders described as follows:

1. **Training:** This is the initial folder to open, providing essential information, including a video tutorial explaining how to use the toolchain. If additional assistance is required, please contact the author at the institutional email: S303635@polito.it

2. **MLP_toolchain:** This folder houses the core toolchain. Do not open or modify it; include it in your MATLAB path or copy it to your working directory. Preserve the folder structure as it was built.

3. **Examples:** Explore practical examples such as classification (digits) and nonlinear regression (2-variable function regression) tasks. These examples serve as starting points for implementing your MLP models. Open this folder after watching the video explanation in the training folder.

## Usage

Detailed instructions on usage are provided in the training folder. Refer to it and ensure that everything mentioned there is installed or included in the MATLAB path before working with the toolchain.

## Copyright and License

This toolbox is authored by Marino Massimo Costantini. You are free to use, modify, and distribute the code for educational and non-commercial purposes. For more details, please refer to the accompanying license file.
