Every AI model is a machine for taking one derivative.
F2 · The Slope of Everything
Learning is one question asked over and over: if I nudge this number, does the error go up or down? This chapter builds that question from a picture rather than a definition. You will zoom in on a curve until it turns into a straight road, meet the chain rule as a row of gears, and read a gradient as an arrow that points uphill. By the end you can say what a model is doing when it trains, check a gradient by hand, and tell a real bottom from a saddle.
- The Question Every Model Asks
- How Fast, Not How Much
- Zoom In Until It Is Straight
- The Derivative Is a Slope at a Point
- The Rules You Will Actually Use
- The Chain Rule Is the Whole Game
- One Knob at a Time
- The Gradient Points Uphill
- Bottoms, Tops and Saddles
- Every Model Is a Derivative Machine
- Measure It Both Ways
- A Taste of Area