Model Training
Continuous Learning
An AI system that keeps learning and improving from new data after deployment, instead of being frozen at launch.
Definition
Also called online learning or lifelong learning. A training paradigm where models are updated incrementally with new data in production, rather than requiring full retraining. Includes safeguards against catastrophic forgetting.
Why it matters
Critical for domains where data distributions shift rapidly, like fraud detection, recommendation systems, and market analysis.
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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