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Attention for Neural Networks, Clearly Explained!!!
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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!!!
If you'd like to support StatQuest, please consider...
...or...
...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
...or just donating to StatQuest!
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
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
If you'd like to support StatQuest, please consider...
...or...
...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
...or just donating to StatQuest!
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
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
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