Phase 2 · Math, when you need it · Linear algebra
Matrices
Tables of numbers that hold datasets and model weights.
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Why it matters
A spreadsheet of customers, a batch of AI embeddings, the settings inside a neural network: all of them are matrices, which is just the maths word for a table of numbers. AI code almost never deals with records one at a time. It puts them all in one table and works on the whole thing at once. Being comfortable with rows and columns is what makes AI code readable instead of mysterious.
What you'll be able to do
- Read a matrix's shape and its entries in math notation and in NumPy
- Treat a dataset as a matrix: rows are examples, columns are features
- Select rows and columns, and compute per-column statistics with axis
- Standardize the columns of a data matrix so features are comparable
Before this lesson
Next in this phase: Matrix multiplication