Transformer Architecture (Part 3): Multi-Head Attention

1. 1Implement self-attention mechanisms using query, key, and value vectors
2. 2Apply multi-head attention by splitting the input into multiple attention heads
3. 3Configure the number of attention heads and the dimensionality of each head
4. 4Test the performance of the multi-head attention mechanism on a benchmark dataset
5. 5Compare the results with single-head attention to evaluate the improvement

Machine learning engineers and researchers can benefit from understanding multi-head attention to enhance their model's capabilities

💡 Multi-head attention allows the model to jointly attend to information from different representation subspaces

Boost your transformer's performance with multi-head attention!