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Transformers (Beyond Basic Attention)
While often conflated with attention, the Transformer architecture is a distinct neural network design primarily built with self-attention mechanisms and feed-forward layers, eliminating recurrence. Its encoder-decoder structure, processing input sequences in parallel, revolutionized natural language processing and now extends to vision and other domains.
In plain terms
Instead of reading a book word-by-word (RNNs), a Transformer scans the entire page, cross-referencing every word with every other word simultaneously to understand context.
Why it matters
Their parallel processing and superior contextual understanding have made them the dominant architecture for complex sequence modeling tasks.
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