Backpropagation
A training algorithm used in neural networks that adjusts weights by propagating errors backward from the output layer to minimize loss.
An iterative optimization process where, after a forward pass computes the network’s predictions, the difference between predicted and true values (the loss) is propagated backwards - layer by layer - to compute gradients. These gradients inform weight updates via gradient descent, enabling deep networks to learn complex, hierarchical feature representations.
In image classification, a convolutional neural network uses backpropagation on millions of labeled photos: after each batch, it tweaks millions of connection weights so that “cat” images produce higher activation in the correct output node and lower activation elsewhere, gradually reaching >95% accuracy on validation data.
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