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The Math Behind Every LLM, in Five Steps: Vectors to Cross-Entropy Loss
One continuous derivation connects vectors, matrices, gradients, and probability into the exact training objective behind every modern language model. Here’s the full arc, with the reasoning that forces each step.
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Matrices — Organized, Simultaneous Dot Products (Ep:02.01)
A matrix is just organized, simultaneous dot products. Module 2 of From Zero to Agents builds matrix multiplication from first principles and uses it to fully decode the real attention…
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Vectors, Norms, and Dot Products Explained: The Math Behind Every Embedding (Ep.02.00)
Every embedding, similarity score, and attention weight in modern AI comes down to vector operations. Module 2 of From Zero to Agents builds vectors, norms, and dot products from first…
