🧭Learn How Embeddings Encode Meaning
Stop treating embeddings as magic vectors. By the end you'll see meaning as geometry — and design a duplicate-FAQ detector for a 1000-question support corpus that you could actually ship.
Phase 1Meaning as Geometry
Meet the distributional hypothesis and why context is meaning
A word is known by the company it keeps
6 minA word is known by the company it keeps
Coordinates, not labels
6 minCoordinates, not labels
Cosine similarity is the only operation that matters
7 minCosine similarity is the only operation that matters
High-dimensional space behaves nothing like 3D
7 minHigh-dimensional space behaves nothing like 3D
Phase 2Training the Map
Train a tiny word2vec and watch analogies become arithmetic
Word2vec is a guessing game with a side effect
6 minWord2vec is a guessing game with a side effect
Negative sampling makes the math tractable
7 minNegative sampling makes the math tractable
Watch one gradient step move the map
7 minWatch one gradient step move the map
King minus man plus woman lands near queen
7 minKing minus man plus woman lands near queen
Bias is geometry — and you can measure it
7 minBias is geometry — and you can measure it
Phase 3From Words to Documents
Move from words to sentences, documents, and retrieval
Sentence embeddings beat averaged word vectors
7 minSentence embeddings beat averaged word vectors
Bi-encoders are fast — cross-encoders are accurate
7 minBi-encoders are fast — cross-encoders are accurate
ANN indexes turn O(N) similarity into O(log N)
8 minANN indexes turn O(N) similarity into O(log N)
Embedding drift is silent until it isn't
7 minEmbedding drift is silent until it isn't
Phase 4Find the Duplicates
Design a duplicate-FAQ finder for a real support corpus
Design a duplicate-FAQ finder for 1000 questions
20 minDesign a duplicate-FAQ finder for 1000 questions
Frequently asked questions
- How do embeddings actually work?
- This is covered in the “Learn How Embeddings Encode Meaning” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What does it mean for vectors to capture meaning?
- This is covered in the “Learn How Embeddings Encode Meaning” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- Why does 'king - man + woman' land near 'queen'?
- This is covered in the “Learn How Embeddings Encode Meaning” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What's the difference between a bi-encoder and a cross-encoder?
- This is covered in the “Learn How Embeddings Encode Meaning” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How do I use embeddings to find duplicate questions in a FAQ corpus?
- This is covered in the “Learn How Embeddings Encode Meaning” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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