what_are_embeddings

Why we included this project

For engineers who want a mental model of how embeddings work before trusting them in a real system, this long-form survey earns its keep. It's a book-length LaTeX document, with Jupyter notebooks alongside, that traces the path from TF-IDF and one-hot encoding through Word2Vec to transformers and generative methods, so you get the historical context plus the usage patterns that actually show up in industry. People already calling embedding APIs will get a clearer sense of why their vectors behave the way they do; newcomers get a fundamentals-first walkthrough that doesn't assume prior deep learning experience. A compiled PDF and a generated website let you read it in whatever format you prefer, and the notebook code means you can follow along interactively. It's teaching material, not deployable software, but it's exactly the kind of grounding that helps teams make better calls about representation learning down the road.