sparsity

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

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

Pruning strips redundant weights to make a model smaller and sometimes faster. The signal is the structured-versus-unstructured divide and why only structured pruning dependably cuts latency on commodity hardware.