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Interactive 3D/Temporal Convolutional Network
HUD
RF = 31 positions
Conv params ≈ 12 weights
RNN: RF grows linearly
TCN: RF grows exponentially
TCN - Dilated Convolutions
Receptive field grows exponentially with depth
Layers
4 layers4
Kernel Size
k = 3
Dilation Base
base = 2
RF Formula
RF = (k−1) × Σd_i + 1
With k=3, d=[1,2,4,8]:
RF = (3−1) × 15 + 1 = 31
How It Works
Each TCN layer is a causal 1D convolution with dilation d. Dilation skips d−1 positions between kernel taps, exponentially expanding the receptive field without increasing parameters.

vs RNN: TCN processes all positions in parallel (no sequential dependency). Training is faster and gradients don't vanish.
Legend
Target node (top, center)
Input in receptive field
Output layer nodes
Other positions

Temporal Convolutional Network - Interactive Visualization

Temporal Convolutional Networks replace recurrence with dilated causal convolutions. Causal means the network cannot look at future time steps - each output only depends on current and past inputs. Dilation exponentially expands the receptive field: with dilation rates 1, 2, 4, 8, a stack of 4 layers covers 16 time steps. TCNs train faster than LSTMs (fully parallel) and often match or exceed them on sequence tasks.

  • See causal convolution: the leftward-only connectivity pattern that ensures no future information leaks into predictions
  • Watch dilation in action: dilation=2 skips every other input, dilation=4 skips every 3 - receptive field doubles with each layer
  • Compare receptive field growth: 4 layers with dilation 1,2,4,8 vs 4 layers of standard convolution - the difference is dramatic
  • Understand why TCNs train faster than RNNs: all time steps are computed in parallel, no sequential dependency
  • Learn residual connections in TCNs: skip connections from input to output of each block prevent vanishing gradients

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.