A comprehensive neural network implementation that leverages cutting-edge deep learning techniques to achieve state-of-the-art handwritten digit recognition.
Supports multiple model architectures, extensive performance analysis, and advanced visualization techniques.
One-click FREE deployment of your digit recognition models.
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🌟 Pioneering the future of computer vision and deep learning. Built for researchers and practitioners.
[!TIP] This project demonstrates three distinct neural network architectures with comprehensive performance analysis.
MNIST Handwritten Digits Dataset - 28x28 Grayscale Images
Neural Network Architectures and Performance Comparison
📊 More Visualizations
Training Progress and Validation Accuracy
Detailed Error Analysis and Confusion Matrix
Tech Stack Highlights:
Important
This project demonstrates modern deep learning practices with TensorFlow/Keras. It combines comprehensive data analysis with multiple neural network architectures to provide state-of-the-art digit recognition. Features include 99.71% accuracy, advanced visualization, and detailed performance analysis.
📑 Table of Contents
- 🧠 MNIST Neural Network AnalysisAdvanced Handwritten Digit Recognition with Deep Learning
We are passionate researchers creating next-generation computer vision solutions. By adopting modern deep learning practices and cutting-edge neural network architectures, we aim to provide researchers and practitioners with powerful, scalable, and interpretable digit recognition models.
Whether you're a student, researcher, or industry professional, this project will be your machine learning playground. Please note that this project demonstrates research-quality implementations with extensive documentation and analysis.
Note
- Python 3.7+ required
- TensorFlow 2.0+ for deep learning capabilities
- Jupyter Notebook for interactive analysis
- GPU support optional but recommended for training
 |
No installation required! Experience our models through the interactive demo. |
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Tip
⭐ Star us to receive all release notifications and stay updated with the latest research!
Experience three distinct neural network approaches, each optimized for different use cases. Our comprehensive implementation provides unprecedented flexibility and performance analysis through advanced deep learning methodologies.
Three distinct model architectures with performance trade-offs
Available Models:
- 🧠 Basic MLP Model: Simple yet effective multi-layer perceptron
- ⚡ Optimized Model: Feature-selected, resource-efficient implementation
- 🚀 Deep CNN Model: Advanced convolutional neural network with state-of-the-art accuracy
Revolutionary analysis toolkit that transforms how researchers understand model behavior. With our advanced visualization algorithms and intuitive metrics, users can gain deep insights while maintaining research-grade accuracy.
Comprehensive Analysis - Data Exploration (left) and Performance Metrics (right)
Analysis Features:
- Data Exploration: Complete MNIST dataset analysis with class distribution
- Feature Importance: Advanced pixel importance visualization and analysis
- Performance Metrics: Detailed accuracy, precision, recall, and F1-score analysis
Beyond the core models, this project includes:
- 🎯 99.71% Accuracy: State-of-the-art digit recognition performance
- 📊 Comprehensive Visualization: Beautiful plots and interactive analysis
- 🔍 Sensitivity Analysis: Advanced model behavior understanding
- ⚡ Performance Optimization: Multiple speed/accuracy trade-off options
- 📈 Training Monitoring: Real-time training progress and validation tracking
- 🧪 Error Analysis: Detailed misclassification investigation
- 🔬 Feature Selection: Advanced dimensionality reduction techniques
- 📝 Research Documentation: Comprehensive methodology and results documentation
✨ More features are continuously being added as deep learning research evolves.
Python 3.7+ |
TensorFlow 2.0+ |
Keras |
NumPy |
Pandas |
Jupyter |
Core Dependencies:
- Deep Learning: TensorFlow 2.0+ with Keras API
- Data Processing: NumPy, Pandas for efficient data manipulation
- Visualization: Matplotlib, Seaborn, Plotly for comprehensive plotting
- Analysis: scikit-learn for advanced metrics and feature selection
- Environment: Jupyter Notebook for interactive development
Development Tools:
- Version Control: Git with comprehensive commit history
- Documentation: Markdown with detailed analysis
- Code Quality: Clean, well-documented, research-grade implementation
- Reproducibility: Fixed random seeds for consistent results
Tip
Each dependency was carefully selected for research compatibility, performance, and educational value.
graph TB
subgraph "Input Layer"
A[MNIST 28x28 Images]
end
subgraph "Basic MLP Model"
B1[Flatten Layer]
B2[Dense 128/256/512]
B3[Dropout]
B4[Output Layer 10]
end
subgraph "Optimized Model"
C1[Feature Selection]
C2[Reduced Dimensions]
C3[Efficient Dense Layers]
C4[Output Layer 10]
end
subgraph "Deep CNN Model"
D1[Conv2D Layers]
D2[MaxPooling]
D3[Batch Normalization]
D4[Dense Layers]
D5[Output Layer 10]
end
A --> B1
A --> C1
A --> D1
B1 --> B2 --> B3 --> B4
C1 --> C2 --> C3 --> C4
D1 --> D2 --> D3 --> D4 --> D5
mnist-handwritten-digit-recognition-project/
├── hand-written-digit-recognition_final.ipynb # Main analysis notebook
├── hand-written-digit-recognition_final copy.ipynb # Backup/experimental
├── README.md # Project documentation
├── LICENSE # MIT License
├── CODE_OF_CONDUCT.md # Community guidelines
├── .gitignore # Git ignore rules
└── .github/ # GitHub configurations
| Model | Accuracy | Prediction Time | Parameters | Use Case |
|---|---|---|---|---|
| Basic MLP | 99.05% | 0.621s | 407,050 | Balanced performance |
| Optimized | 97.86% | 0.528s | 84,618 | Resource-constrained |
| Deep CNN | 99.71% | 4.869s | 1,015,530 | Maximum accuracy |
Accuracy Achievements:
- 🎯 99.71% test accuracy with Deep CNN model
- 🚀 99.05% accuracy with efficient MLP architecture
- ⚡ 0.528s fastest prediction time with optimized model
- 📊 Comprehensive error analysis and sensitivity testing
Model Efficiency:
- 💨 84,618 parameters in optimized model (79% reduction)
- 🔄 Real-time prediction capabilities
- 📈 Scalable architecture for larger datasets
- 🛡️ Robust performance across all digit classes
Note
Performance metrics measured on standard MNIST test set with consistent evaluation protocols.
Important
Ensure you have the following installed:
- Python 3.7+ (Download)
- pip package manager
- Git (Download)
- [Optional] GPU drivers for accelerated training
1. Clone Repository
git clone https://github.com/ChanMeng666/mnist-handwritten-digit-recognition-project.git
cd mnist-handwritten-digit-recognition-project2. Install Dependencies
# Create virtual environment (recommended)
python -m venv mnist_env
source mnist_env/bin/activate # On Windows: mnist_env\Scripts\activate
# Install required packages
pip install tensorflow>=2.0 numpy pandas matplotlib seaborn scikit-learn jupyter plotly3. Launch Jupyter Notebook
jupyter notebook hand-written-digit-recognition_final.ipynb🎉 Success! The interactive notebook will open in your browser with all models and analysis ready to run.
Create a virtual environment for isolation:
# Using conda
conda create -n mnist python=3.8
conda activate mnist
conda install tensorflow numpy pandas matplotlib jupyter
# Using pip
pip install -r requirements.txt # If requirements.txt existsTip
Use GPU acceleration for faster training by installing tensorflow-gpu if you have CUDA-compatible hardware.
Getting Started:
- Open Notebook - Launch the Jupyter notebook
- Run All Cells - Execute the complete analysis pipeline
- Explore Models - Compare different architectures
- Analyze Results - Review comprehensive performance metrics
# Example: Training the Deep CNN Model
from tensorflow.keras import layers, models
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
# Train the model
history = model.fit(x_train, y_train,
epochs=10,
validation_data=(x_val, y_val))Custom Model Parameters:
# Configure training parameters
config = {
'batch_size': 32,
'epochs': 20,
'learning_rate': 0.001,
'validation_split': 0.1,
'early_stopping_patience': 5
}
# Feature selection for optimized model
n_features = 196 # Reduced from 784 original features- Architecture: Multi-layer perceptron with configurable hidden layers
- Features: 128/256/512 neuron configurations tested
- Performance: 99.05% accuracy, balanced speed/accuracy trade-off
- Use Case: General-purpose digit recognition
- Architecture: Feature-selected efficient neural network
- Features: Advanced dimensionality reduction (784 → 196 features)
- Performance: 97.86% accuracy, fastest prediction time
- Use Case: Resource-constrained environments, mobile deployment
- Architecture: Convolutional Neural Network with multiple layers
- Features: Conv2D, MaxPooling, Batch Normalization, Dense layers
- Performance: 99.71% accuracy, state-of-the-art results
- Use Case: Maximum accuracy applications, research benchmarking
The comprehensive analysis reveals distinct trade-offs between model architectures:
Accuracy Rankings:
- Deep CNN: 99.71% - Highest accuracy with computational cost
- Basic MLP: 99.05% - Excellent balance of performance and efficiency
- Optimized: 97.86% - Fastest with good accuracy for constrained environments
Speed Analysis:
- Optimized Model: 0.528s (fastest prediction)
- Basic MLP: 0.621s (balanced performance)
- Deep CNN: 4.869s (highest accuracy, slower inference)
Resource Utilization:
- Optimized: 84,618 parameters (most efficient)
- Basic MLP: 407,050 parameters (moderate)
- Deep CNN: 1,015,530 parameters (most comprehensive)
Sensitivity Analysis:
- Gradient-based importance mapping
- Perturbation robustness testing
- Occlusion sensitivity analysis
Error Analysis:
- Detailed confusion matrix analysis
- Misclassification pattern identification
- Class-specific performance metrics
We welcome contributions from the deep learning community! Here's how you can help:
1. Fork & Clone:
git clone https://github.com/ChanMeng666/mnist-handwritten-digit-recognition-project.git
cd mnist-handwritten-digit-recognition-project2. Create Branch:
git checkout -b feature/improved-architecture3. Make Changes:
- Follow PEP 8 Python style guidelines
- Add comprehensive documentation
- Include performance benchmarks
- Ensure reproducible results
4. Submit PR:
- Provide clear description of improvements
- Include performance comparisons
- Reference related research papers
- Ensure all analyses run successfully
Research Contributions:
- 🧠 New neural network architectures
- 📊 Advanced visualization techniques
- 🔬 Novel analysis methodologies
- ⚡ Performance optimization strategies
Documentation:
- 📚 Enhanced explanations and tutorials
- 🎓 Educational content for beginners
- 📖 Research methodology documentation
- 💡 Use case examples and applications
This project is licensed under the MIT License - see the LICENSE file for details.
Open Source Benefits:
- ✅ Commercial use allowed
- ✅ Modification and distribution permitted
- ✅ Private use encouraged
- ✅ Research and educational use supported
Chan Meng Deep Learning Researcher & Developer |
Chan Meng
LinkedIn: chanmeng666
GitHub: ChanMeng666
Email: [email protected]
Website: chanmeng.live
Contact Information:
- 📧 Email: [email protected]
- 💼 LinkedIn: chanmeng666
- 🐦 GitHub: ChanMeng666
Empowering researchers and practitioners worldwide
⭐ Star us on GitHub • 📖 Read the Research • 🐛 Report Issues • 💡 Request Features • 🤝 Contribute
Made with ❤️ by the Deep Learning Community
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