The algorithm that makes neural network training possible. It calculates how much each weight in the network contributed to the error, then adjusts them to reduce that error. Works by propagating the error signal backward through the network, layer by layer. The workhorse of deep learning.
The fundamental optimization algorithm used to train neural networks.
A mathematical function that measures how far the model's predictions are from the correct answers.
A computing system loosely inspired by biological brains, consisting of interconnected nodes (neurons) organized in layers.
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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