https://github.com/sankhya007/demo_neural

https://github.com/sankhya007/demo_neural

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• Host: GitHub
• URL: https://github.com/sankhya007/demo_neural
• Owner: sankhya007
• Created: 2025-10-29T16:49:19.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-10-29T17:05:46.000Z (8 months ago)
• Last Synced: 2025-10-29T19:08:02.955Z (8 months ago)
• Language: Python
• Size: 16.6 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# 🧠 Neural Network from Scratch (Pure Python Implementation)

A fully functional **neural network built from scratch in Python**, without relying on deep learning frameworks like TensorFlow or PyTorch.  

This project demonstrates the **core mechanics** behind neural networks — from forward propagation to backpropagation and gradient descent — with clean, modular, and well-documented code.

## 🚀 Features

✅ **Custom Neural Network Class** – Pure Python + NumPy implementation  

✅ **Multiple Activation Functions** – ReLU, Sigmoid, and Tanh  

✅ **Configurable Architecture** – Define any number of layers and neurons  

✅ **Supports Both Tasks** – Classification & Regression  

✅ **Mini-Batch Gradient Descent** – Efficient training  

✅ **Real-Time Training Visualization** – Loss and accuracy plots  

✅ **Comprehensive Evaluation Metrics** – Accuracy, MSE, and R² score  

## 🧩 Project Structure

```

    neural_network_demo/

    ├── neural_network.py      # Core neural network implementation (classification)

    ├── main.py                # Demo for classification and regression

    ├── requirements.txt       # Dependencies

    ├── run.bat                # Run script for Windows

    ├── run.sh                 # Run script for Linux/Mac

    ├── README.md              # Project documentation

    └── .gitignore             # Ignore unnecessary files

```

## ⚙️ Installation

1️⃣ Clone the repository:

```bash

git clone https://github.com/sankhya007/demo_neural.git

cd demo_neural

```

2️⃣ Install dependencies:

```bash

pip install -r requirements.txt

```

## ▶️ Usage

Run the demo:

    ```bash

    python main.py

    ```

You’ll see:

    - Training progress with live updates every 100 epochs  

    - Real-time loss and accuracy visualization  

    - Final evaluation metrics for both classification and regression tasks  

## 📊 Results

- Achieves **~78% accuracy** on multi-class classification datasets  

- Smooth convergence curves for both **loss** and **accuracy**

- Regression model yields strong **R² performance** and low **MSE**  

## 🧠 Concepts Covered

- Forward Propagation  

- Backpropagation  

- Activation Functions (ReLU, Sigmoid, Tanh)  

- Softmax Output for Multi-Class Problems  

- Mini-Batch Gradient Descent  

- Loss Functions (Cross-Entropy & MSE)  

- Model Evaluation Metrics  

## 📈 Example Visualizations

- **Classification:** Loss & Accuracy curves over epochs  

- **Regression:** Predicted vs Actual scatter plot with R² score  

- **Training Diagnostics:** Validation loss tracking

## 💡 Future Improvements

- Add dropout and batch normalization  

- Implement momentum and Adam optimizers  

- Save and load trained model weights  

- Extend to support convolutional layers

## 🧑‍💻 Author

**Sankhyapriyo Dey**  

📧 [GitHub Profile](https://github.com/sankhya007)  

## 🪪 License

This project is released under the **MIT License** – free to use and modify.