🧠 Artificial Neural Network from Scratch

Welcome to this repository! 🎉 It contains an implementation of an Artificial Neural Network (ANN) built entirely from scratch using Python and NumPy. This project is inspired by the awesome concepts and implementation details from the article Understand and Implement an Artificial Neural Network from Scratch by Tinz Twins Hub. 🚀

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📚 Table of Contents

• 🌟 Overview
• 🎉 Features
• 🛠️ Installation
• 🚀 Usage
• 📂 Project Structure
• 🔬 Experiments
• 🙏 Acknowledgments
• 📄 License

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🌟 Overview

This project is all about diving deep into the magic of artificial neural networks! Here’s what you’ll explore:

• 🧩 Perceptron and Feedforward Propagation: The building blocks of neural networks.
• 📉 Gradient Descent for Error Minimization: How the network learns to get better.
• 🔄 Backpropagation to Update Weights: Fine-tuning the model step-by-step.
• 💻 Implementation of ANN with NumPy: Pure, from-scratch coding goodness!

You’ll find examples showing how to:

• Generate a toy dataset 🗃️
• Build and train your neural network ⚙️
• Evaluate its performance 📊
• Play with parameters like learning rate, hidden nodes, and iterations 🔧

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🎉 Features

Here’s what makes this project stand out:

• 🏗️ Simple ANN Architecture: A single hidden layer neural network to keep things beginner-friendly.
• ⚙️ Customizable Parameters: Tweak the learning rate, number of iterations, and hidden layer size with ease.
• 📈 Visualization: Cool plots to track training progress and evaluate the model.
• ✍️ Hands-On Code: Well-commented code so you can follow along every step of the way.

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🛠️ Installation

Let’s get this party started! Follow these steps:

1. Clone the repository:

   git clone https://github.com/Lewys-Tech/Artificial_Neural_Network.git
   cd your-repo-name