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Fig. I — A run, in order

ML Foundations

4 parts, read in order — each one assumes the one before it. Back toall runs orthe full archive.

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The parts

Every impressive thing a neural network does — writing code, recognizing faces, translating languages — comes down to one shockingly simple loop repeated billions of times: make a guess, measure how wrong it was, nudge every parameter a tiny bit in the dire…

  1. How Models Actually Learn: Gradient Descent and Backpropagation from First Principles
  2. The Transformer, Explained the Way I Wish Someone Had Explained It to Me
  3. Scaling Laws: The Physics of Machine Learning
  4. Embeddings: How Meaning Becomes Geometry