Linear transformations are mathematical functions that map input vectors to output vectors while preserving the operations of vector addition and scalar multiplication. This means if you take two input vectors and add them or multiply one by a scalar, the transformation will maintain those relationships in the output. In the context of self-attention and multi-head attention mechanisms, linear transformations are crucial because they help transform the input data into different representations that can be processed in parallel, allowing for more effective learning from complex data structures.
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