Transformers are taking over AI right now, and quite possibly their most famous use is in ChatGPT. ChatGPT uses a specific type of Transformer called a Decoder-Only Transformer, and this StatQuest shows you how they work, one step at a time. And at the end (at 32:14), we talk about the differences between a Normal Transformer and a Decoder-Only Transformer. BAM!
NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests:
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8
Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4
Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8
Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo
If you're interested in learning more about the SoftMax function, check out:
https://youtu.be/KpKog-L9veg
If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0
If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out:
Cosine Similarity: https://youtu.be/e9U0QAFbfLI
Attention: https://youtu.be/PSs6nxngL6k
If you'd like to learn more about Normal Transformers, see: https://youtu.be/zxQyTK8quyY
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0:00 Awesome song and introduction
1:34 Word Embedding
7:26 Position Encoding
10:10 Masked Self-Attention, an Autoregressive method
22:35 Residual Connections
23:00 Generating the next word in the prompt
26:23 Review of encoding and generating the prompt
27:20 Generating the output, Part 1
28:46 Masked Self-Attention while generating the output
30:40 Generating the output, Part 2
32:14 Normal Transformers vs Decoder-Only Transformers
#StatQuest