Core idea
A random variable maps outcomes to numbers; a distribution tells you how likely each value is.
Why this exists in ML
Losses, gradients, and metrics are expectations under (unknown) data distributions.
A random variable maps outcomes to numbers; a distribution tells you how likely each value is.
Losses, gradients, and metrics are expectations under (unknown) data distributions.