Transformers Components Apr 2026

: These add the original input of a layer to its output before normalization, providing a "direct path" for gradients to flow backward during training. 5. Linear and Softmax Layers

It captures complex patterns that the attention mechanism might miss by processing each token's representation independently. 4. Normalization and Residual Connections transformers components

This consists of two linear transformations with a non-linear activation (typically ReLU) in between. : These add the original input of a