Model Training
Hyperparameter Tuning
Adjusting the "settings" of an AI model (like learning speed or network size) to find the best performance.
Definition
The process of optimizing the configuration parameters that control the training process itself (learning rate, batch size, architecture choices). These are set before training begins, unlike model weights.
Why it matters
Can make the difference between a mediocre model and a world-class one, often overlooked in favor of data or architecture.
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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