A quick refresher on the maths behind LLMs: vectors, matrices, projections, embeddings, logits and softmax.

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Explains the fundamental mathematical concepts needed to understand how Large Language Models work, focusing on vectors, matrices, high-dimensional spaces, embeddings, and projections. Covers vocab spaces where logits represent token probabilities, embedding spaces where similar concepts cluster together, and how matrix multiplication enables projections between different dimensional spaces. Demonstrates that neural network layers are essentially matrix multiplications that project between spaces, making LLM inference accessible with high-school level mathematics.

The maths you need to start understanding LLMs

<p>Exactly what I was looking for, thx!</p>
