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Vector Embeddings Explained

Learn how text tokens are mapped to dense vector arrays, representing semantic meanings in multi-dimensional space.

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▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Describe vector representation of words as lists of numbers
  • •Explain how similarity corresponds to proximity in high-dimensional space
  • •Understand how models compute dense embedding vectors

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📂 Subtopics

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What Are Embeddings: From Words to Vectors

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How Embeddings Are Created: From Word2Vec to Modern Sentence Transformers

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Embedding Dimensions: What 768 vs 1536 Means and the Tradeoffs

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Using Embeddings in Practice: Generating Them and Computing Cosine Similarity

Related concepts

python-basicstokenization-basics

Next to learn

vector-search-basics →rag-workflow →
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