Hyperparameters

Hyperparameters are external configuration choices established by engineers prior to model execution or training that govern the learning process and inference sampling behavior.

Real-World Mental Model

“The thermostat temperature setting in a classroom (hyperparameter) versus what knowledge the students absorb (parameters).”

INTERACTIVE ENGINE: HYPERPARAMETERS

Live Reactive

Learning Rate (η): 0.10Optimal Convergence

Parameter Position (w):

Objective Loss L(w) = w²:

16.00

CORE PRINCIPLES

What You Must Understand

Training hyperparameters: Learning rate, batch size, epochs, weight decay, optimizer type.

Inference hyperparameters: Temperature, top-p, max tokens, frequency penalty, presence penalty.

Hyperparameters are not learned via backpropagation; they are selected through experimentation.

Reference Implementation

const inferenceConfig = {
  temperature: 0.2, // Low for consistency
  top_p: 0.95,
  max_tokens: 1024
};

Production Failure Mode

Setting temperature too high in production workflows requiring deterministic JSON parsing.

View Full 26-Topic Curriculum Table

Loading AI Engineering Foundations...