Transformers Components -

These components are critical for training deep architectures by ensuring stability and gradient flow.

: Calculates a "relevance score" between tokens, allowing the model to understand how much focus one word should have on another (e.g., relating "he" to "Tom").

This is the "core" of the architecture, allowing the model to focus on different parts of the input sequence simultaneously. transformers components

This consists of two linear transformations with a non-linear activation (typically ReLU) in between.

: This involves running multiple self-attention operations in parallel, which helps the model capture diverse relationships within the data. 3. Feed-Forward Neural Networks (FFN) This consists of two linear transformations with a

: Projects the decoder's output into a much larger vector (the size of the model's vocabulary).

It captures complex patterns that the attention mechanism might miss by processing each token's representation independently. 4. Normalization and Residual Connections Feed-Forward Neural Networks (FFN) : Projects the decoder's

Following the attention layers, each position in the encoder and decoder is processed by a .