This is my Day 2 of learning AI fundamentals where I will be covering the following concepts:

Vector Embeddings

How Tokenisation and Vector Embeddings relate to each other

Vector embeddings:

Vector embeddings is the process of turning each token id(generated during tokenisation) into high dimensional vector where semantic similarity results into geometric closeness. Think of it like this: dog is closer to puppy, also closer to dog food. But dog is not closer to car or petrol.

When we use embeddings?

Recommendations: Suggest similar songs, videos, movies, products

Search: Get search results when keywords don't match

Cluster: Grouping related things together

A beginner might be confused in terms like: Vector, High Dimensional.

This is an example of a vector: [0.9, 0.8, 0.1]. Array/List/Vector all mean the same thing. 'List' is just a plain english, 'array' is the programming term, vector is the math/ml term.

High Dimensional: Multi-dimensional just means more than 1 - could be 2D, 3D, 10D,...…