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// bridge system
useState()→Model Weights·Event Propagation→Forward Pass·Array.map()→Tensor Operation·React diff→Loss Function L = Σ(y−ŷ)²·transition-duration→Learning Rate η·CSS clamp()→σ(x) Activation·Re-render cycle→Training Epoch·Event bubbling→Backpropagation ∂L/∂w·useCallback→Gradient Caching·Promise.all()→Batch Inference·Redux store→Weight Matrix·DevTools profiler→Loss Landscape·useState()→Model Weights·Event Propagation→Forward Pass·Array.map()→Tensor Operation·React diff→Loss Function L = Σ(y−ŷ)²·transition-duration→Learning Rate η·CSS clamp()→σ(x) Activation·Re-render cycle→Training Epoch·Event bubbling→Backpropagation ∂L/∂w·useCallback→Gradient Caching·Promise.all()→Batch Inference·Redux store→Weight Matrix·DevTools profiler→Loss Landscape·
Bridges/Training data
Structural Bridge

Component props
=
Training data

All Themes // Bridge #1
The connection

Both are external inputs that shape behavior. Props configure a component; training data configures a model. Change the props, the component renders differently. Change the data, the model learns differently.

Why "Structural"?

Structural bridges share the same architecture or pattern, even though the domains differ.

Frontend concept
Component props
View in glossary
ML concept
Training data
View in glossary
Related bridges in All Themes
Component state=Model parameters (weights)
Structural
expect().toBe() assertions=Loss function
Structural
Reconciliation=Gradient descent
Intuition
Animation easing / step size=Learning rate
Intuition
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