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https://github.com/chanmeng666/mnist-handwritten-digit-recognition-project
A comprehensive implementation and analysis of handwritten digit recognition using multiple neural network architectures on the MNIST dataset. Features basic MLP, optimized feature-selected model, and deep CNN approaches with detailed performance comparisons and visualizations.
https://github.com/chanmeng666/mnist-handwritten-digit-recognition-project
cnn computer-vision data-analysis data-visualization deep-learning feature-analysis handwritten-digit-recognition keras machine-learning mlp mnist model-optimization neural-networks python scikit-learn tensorflow
Last synced: 16 days ago
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A comprehensive implementation and analysis of handwritten digit recognition using multiple neural network architectures on the MNIST dataset. Features basic MLP, optimized feature-selected model, and deep CNN approaches with detailed performance comparisons and visualizations.
- Host: GitHub
- URL: https://github.com/chanmeng666/mnist-handwritten-digit-recognition-project
- Owner: ChanMeng666
- License: mit
- Created: 2024-10-23T02:52:35.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-07T07:13:17.000Z (about 1 month ago)
- Last Synced: 2024-12-07T08:17:37.579Z (about 1 month ago)
- Topics: cnn, computer-vision, data-analysis, data-visualization, deep-learning, feature-analysis, handwritten-digit-recognition, keras, machine-learning, mlp, mnist, model-optimization, neural-networks, python, scikit-learn, tensorflow
- Language: Jupyter Notebook
- Homepage: https://github.com/ChanMeng666/MNIST-Handwritten-Digit-Recognition-Project/blob/main/hand-written-digit-recognition_final.ipynb
- Size: 1020 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
MNIST Neural Network Analysis
# Features
- **Multiple Model Architectures**: Implementation of basic MLP, optimized feature-selected, and deep CNN models
- **Comprehensive Analysis**: Detailed data exploration, visualization, and performance metrics
- **Advanced Techniques**: Feature importance analysis, sensitivity testing, and error analysis
- **Performance Optimization**: Model comparison and trade-off analysis
- **Interactive Visualizations**: Detailed charts and graphs for model behavior understanding
- **Extensive Documentation**: Step-by-step explanations and implementation details
## Model Performance
| Model | Accuracy | Prediction Time | Parameters |
| --------- | -------- | --------------- | ---------- |
| Basic MLP | 99.05% | 0.621s | 407,050 |
| Optimized | 97.86% | 0.528s | 84,618 |
| Deep CNN | 99.71% | 4.869s | 1,015,530 |
## Technologies Used
## Key Features in Detail
### 1. Data Analysis & Preprocessing
- Extensive MNIST dataset exploration
- Class distribution analysis
- Feature importance visualization
- Robust preprocessing pipeline
### 2. Model Implementations
- **Basic MLP Model**
- Simple yet effective architecture
- Multiple neuron configurations tested
- Baseline performance establishment
- **Optimized Model**
- Feature selection optimization
- Resource-efficient implementation
- Fast prediction times
- **Deep CNN Model**
- Advanced architectural design
- State-of-the-art accuracy
- Comprehensive performance analysis
### 3. Analysis Tools
- Detailed performance metrics
- Sensitivity analysis
- Error visualization
- Model behavior exploration
## Getting Started
### Prerequisites
```python
python>=3.7
tensorflow>=2.0
numpy>=1.19
pandas>=1.1
matplotlib>=3.3
scikit-learn>=0.24
```
### Installation
```bash
# Clone the repository
git clone https://github.com/ChanMeng666/mnist-handwritten-digit-recognition-project.git
# Navigate to project directory
cd mnist-handwritten-digit-recognition-project
# Install required packages
pip install -r requirements.txt
```
### Usage
```python
# Run the Jupyter notebook
jupyter notebook hand-written-digit-recognition_final.ipynb
```
## Results Visualization
The project includes extensive visualizations of:
- Model performance comparisons
- Feature importance maps
- Error analysis
- Sensitivity studies
- Training progression
## Future Improvements
- Implementation of additional architectures
- Enhanced data augmentation techniques
- Model compression exploration
- Real-time prediction capabilities
- Transfer learning experiments
## Contributing
Contributions are welcome! Please feel free to submit pull requests.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Author
[Chan Meng](https://github.com/ChanMeng666)
## Acknowledgments
- The MNIST database creators
- TensorFlow and Keras teams
- Scientific Python community
- All contributors and supporters
## Contact
Chan Meng - [email protected]
Project Link: https://github.com/ChanMeng666/mnist-handwritten-digit-recognition-project