A numerical value in a neural network that determines the strength of the connection between neurons.
A numerical value in a neural network that determines the strength of the connection between neurons. During training, weights are adjusted through backpropagation to minimize errors. The collection of all weights IS the model — when you download model weights, you're getting the trained knowledge.
A value the model learns during training — specifically, the weights and biases in neural network layers.
A computing system loosely inspired by biological brains, consisting of interconnected nodes (neurons) organized in layers.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
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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