This content is part of a series explaining transforms, focusing on “Attention” in this article. The author emphasizes understanding attention without using scientific jargon like keys, queries, and values. Attention is described as abstraction, allowing higher layers in the architecture to operate on relations, grammar, and semantics rather than raw words. The article explains the simple math behind attention, showing how sentences are represented as vectors and how attention is computed using similarity matrices. Trainable attention is discussed, introducing weights to allow the model to learn different sets of rules. By using multiple attentions and adding more attention layers, the model can learn complex rules. The notebook linked in the content demonstrates training an Arabic language embedding using word embedding, positional encoding, and attention through the masking task.
Source link
Source link: https://ahmad-mustapha.medium.com/attention-as-never-explained-before-09b471091e7d?source=rss——large_language_models-5
Unveiling the Secrets of Attention Like Never Before #Focus
