Machine learning models often suffer from overfitting, leading to poor performance on unseen data. This project evaluates different regularization techniques to improve model generalization.
- Analyze overfitting in neural networks
- Compare different regularization techniques
- Evaluate model performance improvements
- Python
- TensorFlow / Keras
- Scikit-learn
- L1 Regularization
- L2 Regularization
- Dropout
- Batch Normalization
- Dropout significantly reduces overfitting
- L2 regularization stabilizes training
- Proper regularization improves model performance
- Helps build reliable ML models for production
- Reduces risk of inaccurate predictions
Sanman Kadam