100 Facts About Building a Robust Neural Network

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Here are 100 facts about building a robust neural network:

1. Data Quality Matters: The quality of your training data significantly impacts the model’s performance.
2. Data Preprocessing: Proper preprocessing, including normalization and handling missing values, is crucial.
3. Feature Scaling: Normalize input features to a similar scale for stable training.
4. Outlier Detection: Identify and handle outliers in your dataset to prevent them from skewing the model.
5. Data Augmentation: Increase training dataset diversity by applying transformations like rotation or flipping.
6. Validation Set: Split your data into training and validation sets to assess model performance on unseen data.
7. Cross-Validation: Use techniques like k-fold cross-validation for more robust model evaluation.
8. Early Stopping: Monitor validation performance and stop training when it plateaus to prevent overfitting.
9. Regularization: Apply techniques like L1 or L2 regularization to avoid overfitting.
10. Dropout: Use dropout layers to randomly deactivate neurons during training, preventing over-reliance on specific features.
11. Batch Normalization: Normalize layer…