concept~180 mindifficulty

Word Embeddings

Embeddings replace sparse one-hot vectors with dense vectors that capture meaning, enabling analogies and powering every modern NLP model.

1

Learn it

Word embeddings map words to dense vectors where geometric proximity reflects semantic similarity. You will study word2vec's skip-gram and CBOW objectives and negative sampling, then load pretrained vectors to compute nearest neighbours and analogies like king - man + woman.

By the end you’ll be able to

  • Explain distributional semantics and dense vector representations
  • Use pretrained embeddings and measure similarity with cosine distance

Free resources

2

Practice

Load pretrained embeddings, find the 10 nearest neighbours of three words, and test one analogy arithmetic query.

3

Build it — your output

A short notebook demonstrating similarity queries and one analogy using pretrained embeddings.

4

Prove it — check yourself

You’re ready to move on when you can honestly say yes to each:

  • Explain why the 'distributional hypothesis' justifies embeddings
  • Describe skip-gram vs. CBOW training targets
  • Interpret a cosine similarity score between two word vectors

Do it with AI

Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).

The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.

  • Understand itClaude / ChatGPT / Gemini

    I'm a beginner learning "Word Embeddings". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.

  • PracticeClaude / ChatGPT / Gemini

    Give me 3 short practice problems on "Word Embeddings" (my goal: explain distributional semantics and dense vector representations), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.

  • Get unstuckClaude / ChatGPT / Gemini

    I'm stuck on this exercise about "Word Embeddings": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.

  • Build & reviewCursor / Copilot

    Here is my work for a "Word Embeddings" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.

  • Prove itClaude / ChatGPT / Gemini

    Quiz me on "Word Embeddings". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.

My notes