3 lessons
Vectors and embeddings, from absolute zero
How a word becomes a list of numbers that captures its meaning, and why that list is the single most important data structure in modern AI.
How embeddings are actually learned
The training trick behind word2vec and every modern embedding model — predicting context from words, and why that alone is enough to teach a model meaning.
Measuring similarity: the actual math
Cosine similarity, dot product, and Euclidean distance — the three ways retrieval systems decide "how close" two pieces of meaning are, worked out by hand and in code.