Model Training
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
Definition
A regularization technique that randomly deactivates a percentage of neurons during each training step, preventing the network from over-relying on any single neuron. Reduces overfitting.
Why it matters
One of the simplest and most effective techniques for building robust neural networks.
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.
Epoch
One complete pass through the entire training dataset, the AI sees every example once per epoch.
From vocabulary to outcomes
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