sparsity

A near-universal ML fundamentals question. Anyone can say 'L1 is lasso, L2 is ridge'; the signal is the gradient-and-geometry reason L1 drives weights to exactly zero and when you'd pick each. Here is that answer.

High-dimensional data breaks the intuitions and methods that work in two or three dimensions. The signal is naming the concrete effects (distances concentrate, data goes sparse, overfitting climbs) and the mitigations that actually move the needle.

Pruning removes redundant weights to shrink and sometimes speed up a model. The signal is the structured-versus-unstructured split and why only structured pruning reliably wins latency on commodity hardware.