Backpropagation
Backpropagation is a core algorithm used to train artificial neural networks efficiently. It calculates the gradient of the loss function with respect to each weight in the network, propagating the error backward from the output layer to the input layer. This gradient information is then used by an optimization algorithm, like gradient descent, to adjust the weights and improve the model's performance by minimizing the error.
Imagine a coach reviewing a team's performance, identifying where mistakes were made, and then telling each player how to adjust their specific actions to improve for the next game.
It's the fundamental mechanism enabling complex neural networks to learn from data and achieve impressive results in AI.
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