A mathematical function that measures how far the model's predictions are from the correct answers.
A mathematical function that measures how far the model's predictions are from the correct answers. The training process aims to minimize this loss. Cross-entropy loss is standard for classification; mean squared error for regression. The choice of loss function shapes what the model optimizes for.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
The fundamental optimization algorithm used to train neural networks.
The algorithm that makes neural network training possible.
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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