Model Training
Gradient Descent
The AI learning process, adjusting its dials a tiny bit at a time, always moving toward less error, like rolling a ball downhill.
Definition
An optimization algorithm that iteratively adjusts model parameters in the direction that reduces the loss function. Variants include SGD, Adam, and AdaGrad. The fundamental mechanism by which neural networks learn.
Why it matters
The core algorithm that makes all neural network training possible, the engine under every deep learning model.
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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