Transformers Components -

: These convert discrete tokens (words or characters) into fixed-size vectors that capture initial semantic meaning.

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

: Projects the decoder's output into a much larger vector (the size of the model's vocabulary). : These convert discrete tokens (words or characters)

In the final stage of the decoder, the output vectors are transformed into human-readable results. transformers components

The is a deep learning architecture that relies on parallelized attention mechanisms rather than sequential recurrence. Its primary components are organized into an Encoder and a Decoder , which work together to transform input sequences into contextualized representations and subsequently into output sequences. 1. Input Processing: Embedding & Positional Encoding