Model Training
Loss Function
The AI's scorecard, a formula that measures how wrong the model's predictions are, guiding it to improve.
Definition
A mathematical function that quantifies the difference between a model's predictions and the actual target values. The model's training objective is to minimize this function. Common examples include MSE and cross-entropy.
Why it matters
The choice of loss function directly shapes what the model optimizes for, choose wrong and it learns the wrong thing.
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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