Backpropagation
A learning algorithm that propagates prediction error backward through a neural network to compute parameter updates
#Backpropagation#error backpropagation#neural network training
What is backpropagation?
Backpropagation is the method neural networks use to calculate how each parameter contributed to prediction error.
It sends error signals from the output layer back through earlier layers.
How does it work?
A model first runs a forward pass, computes loss, and then applies the chain rule to obtain gradients.
Those gradients guide parameter updates that reduce future error.
Why does it matter?
Backpropagation is foundational to modern deep learning training.
Combined with gradient-based optimization, it enables neural networks to learn complex patterns.
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