Backprop Is Credit Assignment
Describe backpropagation as loss-based credit assignment through the chain rule.
Backpropagation is how the network turns one error number into weight-by-weight update directions. Training starts with a forward pass. The network predicts, the loss function compares prediction with label, and the optimizer needs to know which parameters to change. Backpropagation supplies that information. Loss creates the signal The loss is the number being minimized. If the loss does not match the real task, the network can improve the metric while becoming less useful. The chain rule moves blame backward Backprop computes gradients layer by layer from output toward input. Each weight receives a local update direction based on how it…
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