Model Training
Transfer Learning
Taking an AI trained on one task and reusing its knowledge for a different but related task, like a doctor learning veterinary medicine.
Definition
A technique where a model trained on one task is repurposed for a different but related task. The model transfers learned representations, reducing the need for task-specific training data.
Why it matters
The reason we don't train models from scratch for every task, dramatically reduces data requirements and training time.
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
Ready to put Transfer Learning to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
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