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Unpacking how large language models work under the hood Early view of the next chapter for patrons: Special thanks to these supporters: #thanks To contribute edits to the subtitles, visit Other recommended resources on the topic. Richard Turner's introduction is one of the best starting places: Coding a GPT with Andrej Karpathy Introduction to self-attention by John Hewitt History of language models by Brit Cruise: Paper about examples like the “woman - man” one presented here: ------------------ 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 ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here: #manim All code for specific videos is visible here: The music is by Vincent Rubinetti. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly. Mailing list: Twitter: Instagram: Reddit: Facebook: Patreon: Website:
