The diagnostics that tell you what to fix, and the loop that turns them into decisions.
9h · 12 lessons · 1 challenge
Before this: Neural Networks: the First Pass
Knowing an algorithm is not knowing what to do when it underperforms. This module is the part of the job that separates the two: evaluating honestly, diagnosing bias against variance, establishing a baseline worth beating, and reading a learning curve for what it actually says.
Then error analysis, which is a counting exercise rather than an intuition, and the iterative loop that turns a count into the next experiment.
A failing model, a week of time and six options. Allocate it, then see what each would have bought.
Worth 400 XP.
