A value the model learns during training — specifically, the weights and biases in neural network layers.
A value the model learns during training — specifically, the weights and biases in neural network layers. When we say GPT-4 has hundreds of billions of parameters, we mean that many individual numbers were learned. More parameters generally means more capacity to learn complex patterns, but also more compute needed.
A numerical value in a neural network that determines the strength of the connection between neurons.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
A setting you choose before training begins, as opposed to parameters the model learns during training.
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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