Model Training
Pre-training
The initial, massive training phase where an AI model learns general knowledge from enormous datasets before being specialized.
Definition
The first phase of training a foundation model on a large, diverse dataset to learn general patterns, language understanding, or visual features. Followed by fine-tuning for specific tasks.
Why it matters
The most expensive and resource-intensive phase of AI development, costing tens of millions of dollars for frontier models.
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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