Beginner · every module free · 6 challenges
Arrays, shapes, broadcasting and the loop you should not write. Every lab in this curriculum is written against this.
Vectors, systems, matrices, transformations and eigenvectors, each one arriving before the thing that needs it.
Derivatives, integrals, gradients and the descent that uses them, ending at Newton's method.
Events, Bayes, random variables, distributions and the covariance matrix that PCA and anomaly detection both need.
Sampling, estimation, confidence, hypothesis testing and the A/B test they add up to.
Floating point, overflow, the log-sum-exp trick and the reason your loss became NaN.