Visualizing Attention, a Transformer’s Heart | Chapter 6, Deep Learning

Demystifying attention, the key mechanism inside transformers and LLMs. Instead of sponsored ad reads, these lessons are funded directly by viewers: Special thanks to these supporters: #thanks An equally valuable form of support is to simply share the videos. Demystifying self-attention, multiple heads, and cross-attention. Instead of sponsored ad reads, these lessons are funded directly by viewers: The first pass for the translated subtitles here is machine-generated, and therefore notably imperfect. To contribute edits or fixes, visit ------------------ Here are a few other relevant resources Build a GPT from scratch, by Andrej Karpathy If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic: If you’re interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources. Site with exercises related to ML programming and GPTs History of language models by Brit Cruise,  @ArtOfTheProblem  An early paper on how directions in embedding spaces have meaning: ------------------ Timestamps: 0:00 - Recap on embeddings 1:39 - Motivating examples 4:29 - The attention pattern 11:08 - Masking 12:42 - Context size 13:10 - Values 15:44 - Counting parameters 18:21 - Cross-attention 19:19 - Multiple heads 22:16 - The output matrix 23:19 - Going deeper 24:54 - Ending ------------------ 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: