Thomas Piacentini
← Workshop

01 · Math Foundations

Math Foundations

Original text · English

The intuition behind every AI algorithm, through code, not textbooks.

Start this phase on GitHub

Prerequisites: Complete Phase 0, or confirm that Python 3.11+ and Git work from the repository root.

First lesson: Linear Algebra Intuition

Run this command from the repository root:

bash
python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py

Keep the command, exit code, normalized-vector output, projection residual, and one sentence explaining why a matrix-vector product is a neural-network layer.

Next action: Change one input vector, predict the result, run it again, then continue to Vectors, Matrices and Operations.

Browse the full Phase 1 lesson list or the cross-phase roadmap.

Continue step by step.

Code and artifacts 1
What you take away

Saved in your notebook on this device.

Sources and limits

Reading checkpoints are self-reported. Test results are recorded separately and apply to the executed cases.

AI Engineering from Scratch · 3f9715c · MIT

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