One complete pass through the entire training dataset. Training usually requires multiple epochs — the model sees all data several times to learn patterns effectively. Too few epochs means underfitting; too many can lead to overfitting. The right number depends on dataset size and model complexity.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
The number of training examples processed together before the model updates its weights.
When a model memorizes the training data so well that it performs poorly on new, unseen data.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
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