In this 27‑minute Deep Learning Chapter 5, 3Blue1Brown unpacks how Large Language Models work, visualizing data flow from prediction to sampling and back. Beginning with the cycle of predict, sample, repeat, the video dives inside a transformer, outlines the chapter’s layout, and explains the premise of deep learning. It then explores word embeddings, extends the discussion to embeddings beyond words, shows how unembedding maps hidden states back to tokens, and finishes with a softmax layer tweaked by temperature. The lesson is viewer‑funded, not ad‑sponsored.
Key topics
- Transformer — A neural network architecture that uses self-attention to process sequences, enabling parallel computation and capturing long-range dependencies.