Glossary › Training

Overfitting

When a model learns training data patterns too specifically, failing to generalize to new data. Signs: training loss decreasing while validation loss increases. Fixes: regularization, dropout, early stopping, more data.

Where this is taught

• How Models Leak Training Data — Privacy Attacks on AI — Phase 4: Privacy-Preserving AI
• Bias-Variance Tradeoff & Model Diagnostics — Phase 2: Classical Machine Learning
• Model Inversion & Membership Inference Attacks — Phase 3: Adversarial Machine Learning
• Regularization — Phase 1: Training Neural Networks
• Training Dynamics & Regularization — Phase 3: Deep Learning Core
• Linear Regression — Phase 0: ML Foundations

More in Training

Backpropagation · Batch Size · Fine-Tuning · Gradient Descent · LoRA (Low-Rank Adaptation) · RLHF · DPO (Direct Preference Optimization) · QLoRA

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