🤯 Did You Know (click to read)
Feed-forward networks in Transformers are applied identically across positions, making training highly parallelizable and efficient.
Each encoder and decoder layer includes a position-wise feed-forward network that applies two linear transformations with a ReLU activation in between. This allows the model to capture nonlinear interactions among features extracted by attention, increasing expressive capacity. These layers are applied to each position independently but identically, enabling efficient parallel computation.
💥 Impact (click to read)
Feed-forward layers improve model flexibility, allowing it to learn complex mappings from input to output, essential for language understanding and generation.
Understanding feed-forward layers helps AI practitioners tune and adapt Transformer architectures for tasks beyond NLP, such as vision and multimodal processing.
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