Artificial Neural Networks (ANN) Implementations

The primary focus is on demonstrating core ANN concepts and applications, especially in logic gate simulations such as XOR, AND, OR, and NOT gates.

The repository includes implementations using architectures like Feedforward Neural Networks and Multilayer Perceptrons (MLP), employing methods such as backpropagation and perceptron learning.

🚀 Overview

Artificial Neural Networks (ANNs) are a class of machine learning algorithms inspired by the human brain's structure and functioning. ANNs are widely used in various applications like image recognition, natural language processing, and logic-based decision-making.

This repository showcases fundamental ANN techniques and implementations for logic gate simulations and perceptron models.

Key Implementations:

• XOR Gate Using Backpropagation: A neural network model for learning the XOR logic gate using backpropagation in a multilayered perceptron (MLP).
• XOR Gate Using Feedforward MLP: Implementing XOR logic gate simulation using a feedforward multilayered perceptron.
• AND, OR, and NOT Gates: Perceptron implementations to simulate logical AND, OR, and NOT gates.
• Multiple Methods: Various techniques to implement the same gates, demonstrating different approaches to neural network design and training.

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🔧 Features

• XOR Gate Implementations: Learn how to model the XOR gate using both backpropagation and feedforward networks.
• Logical Gates: Perceptron models to simulate AND, OR, and NOT gates.
• Backpropagation: Implement backpropagation in multilayered perceptrons to optimize neural network training.
• Python Code: All code is written in Python using popular libraries like NumPy for matrix operations and calculations.
• Visualizations: Includes visualizations and graphical representations of the logic gates and neural network outputs.

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🌐 Demo

You can try out the different logic gate implementations by running the provided Python scripts. Each implementation includes detailed comments to guide you through the process.

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🛠️ Technologies Used

• Python 3.x
• NumPy
• Matplotlib (for visualizations)
• Pandas

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📂 Project Structure

Artificial-Neural-Networks/
├── and_gate_method1.py         # Perceptron implementation for AND gate (Method 1)
├── and_gate_method2.py         # Perceptron implementation for AND gate (Method 2)
├── not_gate_perceptron.py      # Perceptron implementation for NOT gate
├── or_gate_perceptron.py       # Perceptron implementation for OR gate
├── XOR Gate Using Backpropagation in Multilayered Perceptron.py  # XOR gate using backpropagation
├── XOR Gate Using Feedforward Multilayered Perceptron.py  # XOR gate using feedforward MLP
├── and_gate.png                # Visualization of the AND gate
├── README.md                   # Project documentation

🚀 Running the Code
Clone the repository:

git clone https://github.com/Someshdiwan/Artificial-Neural-Networks

Navigate to the project directory:

cd Artificial-Neural-Networks

Open and run the Python scripts (e.g., XOR Gate Using Backpropagation in Multilayered Perceptron.py) to see the implementations in action.

🌟 Show Your Support

If you like this project, please consider giving it a ⭐ on GitHub!

🤝 Contributing We welcome contributions to improve the repository! If you have any enhancements, bug fixes, or new ideas for additional neural network applications, feel free to fork the repository, make changes, and submit a pull request.