Model Training
Epoch
One complete pass through the entire training dataset, the AI sees every example once per epoch.
Definition
A single iteration over the entire training dataset during model training. Multiple epochs are typically needed for the model to converge. Too many epochs can lead to overfitting.
Why it matters
A fundamental unit of training progress, monitoring loss across epochs tells you if the model is learning.
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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