Phase 1: Math Foundations

Phase 1: Math Foundations

> 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:

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.

Lessons in this phase

• Linear Algebra Intuition
• Vectors, Matrices & Operations
• Matrix Transformations
• Calculus for Machine Learning
• Chain Rule & Automatic Differentiation
• Probability and Distributions
• Bayes’ Theorem
• Optimization
• Information Theory
• Dimensionality Reduction
• Singular Value Decomposition
• Tensor Operations
• Numerical Stability
• Norms and Distances
• Statistics for Machine Learning
• Sampling Methods
• Linear Systems
• Convex Optimization
• Complex Numbers for AI
• The Fourier Transform
• Graph Theory for Machine Learning
• Stochastic Processes