Applications: From Algebra to AI

Applications: From Algebra to AI#

Note

Source: Python-specific — no equivalent in the C# edition.

Todo

Write the applications section covering:

  • Polynomial evaluation (Horner’s method) as a motivating warm-up; cross-reference the hardware chapter where it was introduced

  • A single perceptron: inputs as a vector, weights as a vector, bias as a scalar, output = step(dot(weights, inputs) + bias)

  • Walk through a concrete example (e.g. AND gate) step by step

  • Show how a layer of neurons is just matrix–vector multiplication

  • Brief discussion of why numpy is essential at real scale (cross-reference the Monte Carlo case study timing results)

  • Optionally: least-squares line fitting as a practical algebra example

  • Review questions / exercises connecting back to the vector and matrix sections