This episode explores the creation of "makemore," a character-level language model built step-by-step. The speaker introduces makemore's functionality, demonstrating its ability to generate unique, name-like strings from a large dataset of names. Against this backdrop, the core concept of character-level language modeling is explained, focusing on predicting the next character in a sequence. More significantly, the episode details the implementation of various language models, ranging from simple bigram models to advanced transformers, culminating in a GPT-2 equivalent. For instance, the speaker meticulously guides the listener through building a bigram model, illustrating data processing, probability calculation, and visualization techniques using PyTorch. The episode concludes by demonstrating how to evaluate model performance using negative log-likelihood loss and introduces the concept of regularization to improve model robustness, highlighting the transition from explicit counting methods to a more flexible neural network approach for future model development. This signifies a shift towards more scalable and adaptable language models in the field of natural language processing.
