Beginner · every module free · 5 challenges
Vectors, matrices, projections and eigendecomposition, always attached to the model they show up in.
Derivatives, gradients, the chain rule, and gradient descent built by hand before autograd does it for you.
Distributions, expectation, Bayes and maximum likelihood — the vocabulary every loss function is written in.
Sampling, confidence intervals, hypothesis tests and the A/B test you will be asked to design in an interview.
Floating point, overflow, log-sum-exp and the reasons a correct formula still returns NaN.