Series
From Matrices to Neural Networks
The full arc in eight parts: the mathematics, the two models that introduce the vocabulary, and the network that generalises them.
Most paths into deep learning start at the framework and work backwards, picking up the mathematics only when something breaks. This one runs the other way, and the payoff is that nothing later in the arc arrives unexplained.
The four movements
The space — vectors and matrices. What a vector is, what a map does to one, and why the arithmetic is shaped the way it is.
The slope — derivatives and gradients. The geometric fact that makes "train a model" mean something concrete: move against the gradient.
The fit — what learning means, then linear and logistic regression. Where loss functions come from, and the full anatomy of a supervised model at the smallest size where it is still complete.
The network — neurons and backpropagation. Stacking the fitted model, discovering what that costs, and the algorithm that makes the cost affordable.
How to read it
In order, if you are learning this for the first time. Each part assumes the ones before it and nothing else, and the dependencies are stated in every post so you can rejoin the arc partway if you already have the earlier material.
Parts still in draft are listed here anyway — the arc is declared in full, so the shape of the whole is visible before the last part is written.