Neural Network Training Project

This repository contains a basic implementation of a single-layer neural network (perceptron) using Python and NumPy. The project showcases how to train a neural network using the backpropagation method.

🧩 Project Structure

📁 AI2
├── src
│   └── AI2.py             # Main code file with the neural network implementation
├── README.md              # Documentation file (this file)
├── requirements.txt       # Dependencies
└── .gitignore             # Git ignore file

🚀 How to Run the Project

1. Clone this repository:

   git clone https://github.com/QUA77I20/AI2.git

2. Navigate to the project folder:

   cd AI2

3. Run the Python script:

   python src/AI2.py

⚙️ Functions and Methods

sigmoid(x)

The sigmoid function is used to map any real value to the range (0, 1).

Training Process

• The network is trained using a simple dataset of binary inputs and outputs.
• The backpropagation method is used to adjust the synaptic weights based on the error between the expected and actual outputs.

📈 Training Example

Initial random weights:

[ 0.5, -0.3, 0.8 ]

After training:

[ 1.2, -0.6, 0.9 ]

🧪 Testing

The network is tested with new inputs to predict the output.

Example test input:

New input: [1, 1, 0]
Predicted output: 0.89

📂 Future Improvements

• Implement multi-layer perceptron (MLP).
• Add error visualization (e.g., matplotlib graphs).
• Optimize the backpropagation algorithm.

📄 License

This project is open-source and available under the MIT License.