Backpropagation
Backpropagation efficiently applies the chain rule through a composed computational graph. It computes gradients; an optimizer such as stochastic gradient descent or Adam decides how to use them for updates.
Overview. Backpropagation efficiently applies the chain rule through a composed computational graph. It computes gradients; an optimizer such as stochastic gradient descent or Adam decides how to use them for updates.
Mechanism and context. Backpropagation organizes the chain rule in reverse through a composed computational graph. Starting from a loss, it reuses intermediate derivatives to assign each parameter a gradient without independently recomputing every possible path through the network.
Practical use. Separate differentiation from optimization when reading or diagnosing a training system. Backpropagation produces gradients; gradient descent or Adam decides how those gradients change parameters. Checking this boundary helps localize incorrect derivatives, unstable values, and ineffective update settings.
Related reading. Continue with Chain rule for the calculus identity, Gradient descent for the update step, and Learning representations by back-propagating errors for the landmark multilayer-network report.
Claims and evidence
Claim ledger, 2Backpropagation efficiently applies the chain rule through a composed computational graph.
It computes gradients; an optimizer such as stochastic gradient descent or Adam decides how to use them for updates.