AI article
Backpropagation from scratch in NumPy: let the shapes do the work
Community description: A two-layer network trained by hand: the forward pass, the gradients as matrix products, and a numerical check that catches your mistakes.
Dev.to | Nov 8, 2026 | geodesic partners
Automated excerpt
Write G_X for ∂L/∂X, which always has the same shape as X. State the shapes before you write any code. Bring up gradient checking yourself, before you're asked.
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