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.
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: Bag of Words, TF-IDF, and Text Representation, Backpropagation from Scratch (Deep Learning from Scratch), 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, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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