🛡️ Fighting Overfitting
Learn powerful techniques to prevent your models from memorizing training data
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Batch Normalization
The Overfitting Challenge
Overfitting occurs when a model learns training data too well, including noise and irrelevant patterns, leading to poor generalization. Regularization techniques help models learn meaningful patterns while staying robust to new data.
🎯 Regularization Goals
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Prevent overfitting: Stop memorizing training data
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Better generalization: Improve validation performance
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Reduce complexity: Simpler, more robust models
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Handle noise: Ignore irrelevant patterns
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Overfitting
Model fits training data too closely
•High train accuracy (99%)
•Low validation accuracy (75%)
•Memorizes noise
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Good Fit
Model learns general patterns
•Good train accuracy (93%)
•Similar validation accuracy (91%)
•Generalizes well
🛠️ Regularization Techniques
Dropout
Randomly disable neurons
L1/L2
Penalize large weights
Early Stopping
Stop when val loss increases
Data Aug
Generate more examples