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What is an Embedding?

“While ‘vectors’ and ’embeddings’ are frequently used interchangeably in machine learning, they don’t always refer to the same concept. In machine learning, models cannot directly understand raw data like text, images, or audio. Instead, they work with numbers. To make this possible, we need to transform complex data into a numerical format that models can process. This transformation is done using embeddings.

An embedding is a way of converting high-dimensional data (like words, sentences, images, or sounds) into numerical vectors. These vectors capture the meaning, context, or characteristics of the data in a form the model can understand. For example, a word embedding places words with similar meanings (like “boat” and “ferry”) closer together in vector space.

What is a Vector?

A vector is simply a list of numbers arranged in order, either as a row or a column. Each number in the vector represents some feature or property of the data.Imagine it like coordinates on a map. Each number is like a direction (north, east, up, down, etc.), and together they describe a specific position in space.

Now, instead of a physical map, imagine a semantic map — where ideas, words, or images are placed. Similar things land close to each other. For example:

• “Boat” and “Ferry” would be near each other.
• “Boat” and “Mountain” would be far apart.

This is how machines use vectors to recognize meaning and relationships: the closer the vectors are in space, the more alike the objects they represent.

In short:

• Embedding = the process of converting data into vectors.
• Vector = the numerical representation of that data.

Here’s a simple visual diagram of how embeddings work:

Here’s a step-by-step graphic showing the journey:

1. Raw Data → words like “Boat,” “Ferry,” “Car,” and “Mountain.”
2. Embedding → the process that translates these words into numbers.
3. Vector->Each word (Boat, Ferry, Car, Mountain) is converted into a vector (coordinates).
4. Vector Space → the words are placed as coordinates on a map of meaning, where related words (Boat & Ferry) are close, and unrelated words (Boat & Mountain) are far apart.

Thus On the“map of meaning,” Boat and Ferry are close together because they’re related. Mountain is far away from them because it has a very different meaning.Car lands in another region, separate from both boats and mountains.