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Explain backpropagation and why deep networks can be trained at all, without writing a wall of calculus.

Backprop is just the chain rule run efficiently in reverse, but the question that separates levels is why a hundred-layer network trains when a naive one wouldn't. The credit-assignment intuition plus the three tricks that keep gradients alive.

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FEDITOR'S NOTE

This is an explainability screen with a depth trap. Anyone can say 'chain rule'; the signal is explaining why a deep stack used to be untrainable and what specifically changed (residual connections, normalization, ReLU). The reserved follow-up is 'why is it called back-propagation and not forward': the answer is that you reuse cached forward activations and avoid recomputing shared subexpressions, which is the whole efficiency win.

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