: Projects the decoder's output into a much larger vector (the size of the model's vocabulary).
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) : Projects the decoder's output into a much
: Vectors are added to the embeddings to provide information about the relative or absolute position of each token in the sequence. 2. The Multi-Head Attention Mechanism The Multi-Head Attention Mechanism : Normalizes the vector
: Normalizes the vector features to keep activations at a consistent scale, preventing vanishing or exploding gradients.
: 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").