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>=3.7
tensorflow>=2.0
numpy>=1.19
pandas>=1.1
matplotlib>=3.3
scikit-learn>=0.24

Installation

# 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

# 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 file for details.

Author

Chan Meng

Acknowledgments

• The MNIST database creators
• TensorFlow and Keras teams
• Scientific Python community
• All contributors and supporters

Contact

Chan Meng - ChanMeng666@outlook.com

Project Link: https://github.com/ChanMeng666/mnist-handwritten-digit-recognition-project