This post explains how large language models (LLMs) function using basic math concepts. It covers various components like neural networks, embeddings, self-attention, softmax, and the GPT and transformer architectures. The approach is highly educational, using simplified explanations and visual aids to make the concepts accessible to those with minimal mathematical background.
Table of contents
Understanding LLMs from scratch using middle school mathWhat will we cover?A simple neural network:How are these models trained?How does all this help generate language?What makes large language models work so well?EmbeddingsSubword TokenizersSelf AttentionSoftmaxResidual connectionsLayer NormalizationDropoutMulti-head AttentionPositional encoding and embeddingThe GPT architectureThe transformer architectureAppendix2 Comments
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