Phase 0: NLP Foundations · ~75 minutes · Python

Word Embeddings — Word2Vec from Scratch

A word is the company it keeps. Train a shallow net on that idea and geometry falls out.

Hiring signal: Understanding of word embeddings — word2vec from scratch internals

What you will learn

Implement word embeddings — word2vec from scratch from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 5 · 02 (BoW + TF-IDF), Phase 3 · 03 (Backpropagation from Scratch) Time: ~75 minutes

The Problem

TF-IDF knows dog and puppy are different words. It does not know they mean nearly the same thing. A classifier trained on dog cannot generalize to a review about puppy. You can paper over this by listing synonyms, but that fails on rare terms, domain jargon, and every language you did not anticipate.

You want a representation where dog and puppy land close together in space. Where king - man + woman lands near queen. Where a model trained on dog transfers some signal to puppy for free.

Word2Vec gave us that space. Two layer neural network, trillion-token training runs, published in 2013. The architecture is almost embarrassingly simple. The results reshaped NLP for a decade.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Embedding