Master Attention in Neural Networks: Simple Explanation for Transformers & ChatGPT 🤖

Attention is one of the most important concepts behind Transformers and Large Language Models, like ChatGPT. However, it's not that complicated. In this StatQuest, we add Attention to a basic Sequence-to-Sequence (Seq2Seq or Encoder-Decoder) model and walk through how it works and is calculated, one step at a time. BAM!!!

NOTE: This StatQuest is based on two manuscripts. 1) The manuscript that originally introduced Attention to Encoder-Decoder Models: Neural Machine Translation by Jointly Learning to Align and Translate: https://arxiv.org/abs/1409.0473 and 2) The manuscript that first used the Dot-Product similarity for Attention in a similar context: Effective Approaches to Attention-based Neural Machine Translation https://arxiv.org/abs/1508.04025

NOTE: This StatQuest assumes that you are already familiar with basic Encoder-Decoder neural networks. If not, check out the 'Quest: https://youtu.be/L8HKweZIOmg

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0:00 Awesome song and introduction
3:14 The Main Idea of Attention
5:34 A worked out example of Attention
10:18 The Dot Product Similarity
11:52 Using similarity scores to calculate Attention values
13:27 Using Attention values to predict an output word
14:22 Summary of Attention

#StatQuest #neuralnetwork #attention