Components — Transformers

Since Transformers do not process data sequentially like RNNs, they must explicitly "learn" the order of words.

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

: 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) Since Transformers do not process data sequentially like

: 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"). preventing vanishing or exploding gradients.

: Normalizes the vector features to keep activations at a consistent scale, preventing vanishing or exploding gradients.