The Spelled-Out Intro to Language Modeling: Building Makemore – Full Timestamps & Chapters
[0:00] Introduction to Make More Andrej introduces the Make More project, explaining it as a character-level language model that generates name-like text from a dataset of 32,000 names, and previews the neural network architectures to be covered, from bigrams to transformers. [1:00] Loading and Exploring the Dataset The transcript covers loading the names.txt dataset into a Python list, examining its properties like word count and length distribution, and setting up the foundation for building language models. [3:00] Bigram Language Model Basics Andrej explains the bigram language model concept, where only two characters are considered at a time to predict the next character, and demonstrates how to extract bigrams from the dataset using Python. [5:00] Counting Bigrams with a Dictionary The tutorial shows how to count bigram occurrences using a Python dictionary, then transitions to a more efficient 2D array representation using PyTorch tensors for storing counts. [7:00] Building the Count Matrix Andrej constructs a 28x28 count matrix (later reduced to 27x27) with character-to-integer mappings, visualizes the counts using Matplotlib, and explains the structure including special start and end tokens. [10:00] Sampling from the Bigram Model The video demonstrates how to convert counts to probabilities, use torch.multinomial for sampling, and generate names by iteratively sampling characters based on the previous character's probability distribution. [13:00] Evaluating Model Quality with Log Likelihood Andrej introduces the concept of likelihood and log likelihood as a measure of model quality, explaining how to evaluate the probability assigned to training data and why log likelihood is preferred for mathematical convenience.
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