Unpacking how large language models work under the hood
Early view of the next chapter for patrons: https://3b1b.co/early-attention
Special thanks to these supporters: https://3b1b.co/lessons/gpt#thanks

To contribute edits to the subtitles, visit https://translate.3blue1brown.com/

Other recommended resources on the topic.

Richard Turner's introduction is one of the best starting places:
https://arxiv.org/pdf/2304.10557.pdf

Coding a GPT with Andrej Karpathy
https://youtu.be/kCc8FmEb1nY

Introduction to self-attention by John Hewitt
https://web.stanford.edu/class/cs224n/readings/cs224n-self-attention-transformers-2023_draft.pdf

History of language models by Brit Cruise:
https://youtu.be/OFS90-FX6pg

Paper about examples like the “woman - man” one presented here:
https://arxiv.org/pdf/1301.3781.pdf

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Timestamps

0:00 - Predict, sample, repeat
3:03 - Inside a transformer
6:36 - Chapter layout
7:20 - The premise of Deep Learning
12:27 - Word embeddings
18:25 - Embeddings beyond words
20:22 - Unembedding
22:22 - Softmax with temperature
26:03 - Up next

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https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u

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