Learning Language Rules
Timothy highlights the evolution of language model training, revealing how early on, simpler rules like bigrams and trigrams serve as effective predictors. As training progresses, however, the model discards these simplistic approaches in favor of more complex rules, illustrating a shift from basic template matching to a deeper understanding of language structure. This transition underscores the importance of adapting learning strategies over time to minimize prediction errors.In this clip
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Machine Learning Street Talk (MLST)
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