📍 Embeddings Explained Like You're 5
Building AI systems and writing about how they actually work. Master of AI @ University of Technology Sydney. Previously B.Tech CS with focus on IoT. I believe the best way to learn is to explain. That's why I'm documenting tech concepts with simple analogies (@sreekarreddy.com). AWS Certified • Azure AI Certified • Neo4j Professional • Google Data Analytics When not coding: exploring Sydney, working on side projects, and teaching tech to anyone who'll listen.
GPS coordinates for words
Day 4 of 149
👉 Full deep-dive with code examples
The Map Analogy
How do you describe where Sydney is?
Option 1: "It's in Australia, on the east coast, near the ocean..."
Option 2: GPS: (latitude, longitude)
The GPS coordinates are precise and comparable:
- Another city:
(latitude, longitude) - You can calculate the distance between them.
Embeddings are GPS coordinates for words!
The Problem
Computers see words as random symbols:
- "dog" = random ID #4521
- "puppy" = random ID #8293
But wait... "dog" and "puppy" are similar! How would a computer know?
How Embeddings Work
Convert words to numbers that capture meaning:
"dog" → [x1, x2, x3, ...] (many numbers)
"puppy" → [y1, y2, y3, ...] (many numbers)
"car" → [z1, z2, z3, ...] (many numbers)
Similar words → Similar numbers!
Now you can:
- Measure similarity between words
- Find words with similar meanings
- Group related concepts
Real Example
Search for "good restaurants":
- Turn the query into an embedding (a long list of numbers)
- Compare with all document embeddings
- Find docs that are "close" in meaning
- Return results like: "highly rated dining", "great places to eat"
Even though the exact words differ!
In One Sentence
Embeddings turn words into numbers that capture their meaning, so computers can understand similarity.
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