Linear Algebra in a Tiny Neural Network
Trace every shape, bias, activation, and rank change through a two-layer network with a worked NumPy example and an interactive explorer.
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Trace every shape, bias, activation, and rank change through a two-layer network with a worked NumPy example and an interactive explorer.
See how a translation moves a line without changing its direction, and why a neural layer bias can move a classifier boundary away from the origin.
Learn to read matrix dimensions and entries, map rows and columns to dense-layer roles, and catch shape or indexing mistakes in NumPy.