January 2025
Intermediate to advanced
698 pages
16h 48m
English
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6.4 Transformer Networks for Sequence Modeling
Traditional RNNs and their variants like LSTMs and GRUs process sequences one step at a time. This sequential nature makes them challenging to parallelize, and they struggle with very long dependencies due to vanishing gradients. Transformers, introduced in the groundbreaking paper Attention Is All You Need (Vaswani et al., 2017), revolutionized sequence modeling by addressing these limitations.
Transformers employ an innovative attention mechanism that processes the entire sequence simultaneously. This approach allows the model to capture relationships between all elements in the sequence, regardless of their position. The attention mechanism computes relevance scores between each pair of elements, ...
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