token-embeddings #

Shows how a discrete token id i selects a row from the embedding matrix E (a learnable lookup table). The selected row E[i] is highlighted and displayed as a length-d vector, while an animated “lookup packet” travels from the token list to the corresponding row. A simple training-like update continuously nudges only the selected row’s values to illustrate embeddings as learned parameters.

canvasclick to interact

⏮◀◀▶▶STEP0.25x1xZOOM

t=0s

practical uses #

01.Turning token IDs into model inputs for transformers/RNNs via embedding lookup
02.Understanding why embedding tables scale with vocabulary size × dimensionality (|V|·d)
03.Building intuition for how training updates only the rows for tokens seen in a batch (sparse parameter updates)

technical notes #

Pure Canvas2D. Uses a closure to maintain an embedding matrix E and performs a small continuous update on the currently selected row to simulate training. Layout and shapes are grid-snapped for a blocky aesthetic; animation cycles via step-based token selection plus eased packet motion.

← diffusion-models reinforcement-learning-introduction →