500 cards
This deck walks you through the core mathematical building blocks behind modern AI, starting with vectors and the ways you can add, scale, and measure them. You'll get comfortable with different norms like L1, L2, and the general Lp, as well as the dot product and its close cousins, cosine similarity and the Cauchy-Schwarz inequality. From there, it moves into matrices, covering their definition, arithmetic operations, and the rules of matrix multiplication.
The material is a great fit if you're beginning a journey into machine learning or deep learning and want a solid grip on the linear algebra that shows up everywhere, from embedding spaces to neural network weights. It's also handy as a refresher for anyone who learned these concepts a while ago and wants to quickly re-establish fluency before tackling more advanced topics like gradients, eigendecomposition, or attention mechanisms.
Because the deck is concept-heavy and full of formulas, try studying it in short, focused sessions rather than long cramming blocks. Spend a moment after each card trying to sketch the idea on paper, even briefly, since visualising vectors and matrices is what really cements these definitions. Spacing your reviews over a few days will also help the terminology and properties feel like second nature rather than memorized definitions.