Summary of Tutorial 6: Transformers and MH Attention (Part 1)

00:00:00 - 00:15:00

The "Tutorial 6: Transformers and MH Attention (Part 1)" video tutorial explains how the transformer architecture works and how it can be used to generate the same features as a word-based recognition algorithm but with a new ordering of the features. This has a big advantage in terms of computational complexity, as compared to word-based recognition algorithms which have a quadratic increase in complexity as the sequence length increases.

• 00:00:00 This tutorial introduces the Transformers model of attention, and explains how it can be used to follow multiple inputs. It also covers the theory behind Transformers, and how to calculate the attention score for a given query and key.
• 00:05:00 The "Tutorial 6: Transformers and MH Attention (Part 1)" video tutorial shows how Transformers and MH attention works. The attention mechanism is called "scaled dot product attention", and it uses a matrix operation to calculate the attention score for a query and key. The video also shows how to visualize the attention graph.
• 00:10:00 The video tutorial explains how to create a transformer architecture using attention. The video also shows how to apply attention to a sequence of words.
• 00:15:00 Transformers can be used to generate the same features as a word-based recognition algorithm, but with new ordering of the features. This has a big advantage in terms of computational complexity, as compared to word-based recognition algorithms which have a quadratic increase in complexity as the sequence length increases.