Vectors, systems, matrices, transformations and eigenvectors, each one arriving before the thing that needs it.
12h · 15 lessons · 1 challenge
Before this: Array Programming & Vectorization
A matrix multiply is the entire forward pass of a linear layer, and a shape error is the most common way a training run dies. This module builds linear algebra so that nothing is used before it has been taught: vectors, then the systems they solve, then matrices, then what matrices do to space, then the determinant, then eigenvectors, then PCA.
You will hand-multiply matrices before you are allowed to call NumPy, because the people who can do that are the people who can debug a shape mismatch in thirty seconds instead of an afternoon.
Twenty broken forward passes, each one dimension wrong. Fix every one in as few edits as you can. The leaderboard ranks by edits, not time.
Worth 400 XP.
