Neuroscience
Backpropagation is a supervised learning algorithm used in artificial neural networks to minimize the error in predictions by adjusting the weights of the connections. It works by calculating the gradient of the loss function with respect to each weight through the chain rule, propagating errors backward from the output layer to the input layer, thus improving the model's accuracy over time. This process mimics how learning can occur in biological neural networks, offering insights into both computational models and brain function.
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