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3.1 Encoder and Decoder Stacks
The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.
We employ a residual connection around each of the two sub-layers, followed by layer normalization. That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)).
The N=6 layers depth was empirically chosen. Deeper networks capture more complex features but are harder to train. ResNet-style residual connections solve the vanishing gradient problem.
Layer Normalization differs from Batch Normalization by normalizing across the features instead of the batch...
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