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Model Atlas · Neural networks

Multilayer Perceptron

A stack of learned affine transformations and nonlinearities that approximates complex functions.

core mathematical viewhₗ=φ(Wₗhₗ₋₁+bₗ)
Mental model

Understand it before memorizing it.

Repeatedly transform features into representations that make the target easier to separate.

Best fit

Where this model earns its place

General nonlinear mapping

Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.

Dense feature learning

Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.

Neural baseline

Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.

Strengths and limits

Trade-offs matter more than popularity.

Strengths

✓ Flexible

✓ Differentiable end-to-end

Limitations

△ Needs tuning/data

△ Less interpretable

Evaluation

Metrics to watch

LossInterpret with the product objective and error cost.
Accuracy/F1Interpret with the product objective and error cost.
CalibrationInterpret with the product objective and error cost.
LatencyInterpret with the product objective and error cost.
Production checklist

Before it reaches real users

  1. 01

    Normalize inputs

    Document the assumption and instrument the condition so regressions can be detected.

  2. 02

    Regularize

    Document the assumption and instrument the condition so regressions can be detected.

  3. 03

    Monitor confidence

    Document the assumption and instrument the condition so regressions can be detected.