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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5articleswith this tag.
Numerical Solving for AI: Use Solve Without Explicit Inverses
Solve AI parameter-recovery systems with NumPy, then use residuals, condition numbers, and singularity checks to judge whether the answer is trustworthy.
Matrix-Vector Products for AI: Two Views of One Dense-Layer Calculation
Compute a matrix times a vector by rows and by columns, implement both views in NumPy, and debug dense-layer shape and operator mistakes.
Vectors as AI Data: How Features Turn a Document Into Numbers
Learn to read row and column vectors, build a document feature vector by hand and in NumPy, and catch shape or feature-order mistakes.
Sigma Notation for AI: How Losses Become One Training Objective
Learn sigma and product notation, expand bounds into loops, and use NumPy sums and means to turn per-example errors into a mean squared error objective.