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deep-learning #

Visualizes a deep network as a layered composition f_θ(x)=f_L(...f_2(f_1(x))). Animated packets flow left-to-right through layer blocks, while each layer’s representation vector h^l lights up as features are transformed into higher-level abstractions. A cycling “inductive bias” panel switches between CNN (locality + weight sharing), RNN (recurrence), and Attention (global mixing), showing how architecture constrains which connections exist and thus which function families are parameter-efficient and generalize well.

canvasclick to interact

⏮◀◀▶▶STEP0.25x1xZOOM

t=0s

practical uses #

• 01.Explaining why depth creates hierarchical feature representations (edges→parts→objects, etc.)
• 02.Comparing architectural inductive biases (CNN vs RNN vs Attention) at a conceptual level
• 03.Teaching forward-pass composition and intermediate activations h^l in deep models

technical notes #

Uses a 3.2s forward-pass phase for smooth highlighting across layers; packets are lightweight stateful elements updated with dt. Blocky aesthetic is enforced via grid snapping and strokeRect/fillRect primitives; bias modes change the rendered connection topology (local+shared kernels, recurrence loop, or global fan connections).

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