The Random Transformer: Understand how transformers work by demystifying all the math behind them

This article explains the mathematics behind a transformer model, specifically focusing on a simplified end-to-end example. It covers the basics of tokenisation, embedding, positional encoding, and the self-attention mechanism, providing insights into how transformers work during inference, especially in language translation. The article is designed to demystify the complex steps and numerous parameters involved in transformer models, making it accessible for those with basic linear algebra and machine learning knowledge. And don't forget The Illustrated Transformer{:target="_blank"}, which is required reading for this article.

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