Overview
Transformer architecture revolutionized the field of natural language processing by introducing a new way to handle sequential data. Unlike traditional models like RNNs, transformers utilize self-attention mechanisms that allow them to weigh the importance of different words in a sentence, leading t...
Key Terms
Example: Neural networks are used in image recognition.
Example: In a sentence, self-attention helps determine which words are most relevant.
Example: Positional encoding helps distinguish between 'cat sat on the mat' and 'the mat sat on cat.'
Example: Multi-head attention can capture various meanings of a word based on context.
Example: Feed-forward networks are used in the final layers of transformers.
Example: The encoder transforms the input sentence into a set of vectors.