The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors. Involves forward passes (making predictions), computing loss (measuring errors), and backpropagation (adjusting weights). Training large models can take weeks on thousands of GPUs and cost millions of dollars.
The initial, expensive phase of training where a model learns general patterns from a massive dataset.
The process of taking a pre-trained model and continuing to train it on a smaller, specific dataset to adapt it for a particular task or domain.
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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