From Character Pairs to Language Prediction: Mastering the Bigram Model

This chapter teaches language modeling fundamentals by implementing a simple bigram model that predicts tokens based on character pair frequencies, using only counting and probability—no neural networks required. This baseline reveals how statistical patterns alone can generate coherent text, and provides a concrete benchmark against which to measure the advantages of more complex approaches. Starting with this elementary model ensures readers understand the prediction task itself before tackling gradient computation and parameter optimization.