Vector Embeddings
After this, you can understand how AI represents meaning as numbers and use embeddings in your own projects.
Since you're familiar with Python, think of vector embeddings like a dictionary (hash map) where the keys are concepts and the values are coordinates in a multi-dimensional space, allowing you to measure distance (similarity) between them.
Definition
A vector embedding is a way to represent words, sentences, images, or any data as a list of numbers (a vector) where similar items are close together in the mathematical space.
💡The Intuition
Imagine a huge library where every book has a location. Instead of organizing by genre on shelves, imagine each book floats in 3D space. Similar books—like two mystery novels—float close together, while a cookbook floats far from a physics textbook.
Now extend this to words: "king" and "queen" would be near each other, while "banana" would be somewhere else entirely. Vector embeddings do exactly this, but in hundreds or thousands of dimensions instead of just three.
The magic is that mathematical operations become meaningful: king - man + woman ≈ queen. This is why embeddings revolutionized AI—they turn meaning into math.
King
Queen
Paris
Tokyo
Words with similar meanings cluster together, and relationships are preserved as directions in the space.
Quick Check
Why might the embedding for "king" be mathematically close to the embedding for "queen"?