Model Training
Overfitting
When an AI memorizes the training data too well and fails on new data, like a student who memorizes answers but can't solve new problems.
Definition
A modeling error where a model learns the training data too precisely, including its noise and outliers, resulting in poor generalization to unseen data. Addressed through regularization, dropout, and cross-validation.
Why it matters
The most common failure mode in machine learning, a model that performs great in testing but fails in production.
Related terms in Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
Distillation (Model Distillation)
Teaching a small, fast AI model to mimic a large, expensive one, so you get similar results at a fraction of the cost.
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
From vocabulary to outcomes
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