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Explore the foundational concepts behind Large Language Models in this 17-minute lecture that breaks down how LLMs like GPT function from both conceptual and mathematical perspectives. Learn what constitutes a Language Model and discover how computers process and understand human language through probability-based approaches. Master the chain rule of probability as it applies to next-word prediction and understand why modern LLMs operate as autoregressive models. Follow along with a detailed step-by-step analysis using the example "The tea is very hot" to see how LLMs learn to predict the next token in a sequence. Gain clear insights into the fundamental mechanism that powers popular models like ChatGPT, Gemini, and Claude, establishing the groundwork for deeper understanding of natural language processing and neural network architectures.
