Join us for this free online session with David Hoyle from dunnhumby as he explores the mathematics behind Transformers and Large Language Models, helping data scientists better understand what is happening under the hood of today’s AI systems.
Large Language Models (LLMs) are everywhere. They are extremely powerful, versatile and useful. However, many data scientists only use them as a tool, not getting to grips with understanding how they work from a computational and mathematical perspective, nor understanding why they give the output that they do. Yet, at their heart LLMs use simple mathematical ideas and tools, such as “attention” and “transformer” blocks. Those mathematical ideas are accessible to every data scientist with a grounding in the basics of linear algebra, probability, and machine learning. In this talk I will provide a high-level explanation of the core mathematics concepts behind LLMs and Transformers, focusing on how they use attention to predict the next token in a sequence of tokens. This 1hr talk is intended to be introductory. It will not make you a prompting expert, it will not make you an expert in training multi-billion parameter foundational models, nor will it make you rich. It will give you a basic grounding of what’s inside an LLM and it will give you more confidence that you know what’s going on under the hood the next time you use an LLM.
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