At the heart of vector databases lies the concept of vector embeddings. These embeddings transform complex data, such as words or images, into numerical vectors. Imagine representing a word or an image not as text or pixels, but as a series of numbers.
These numbers, organized into dimensions, capture various features of the word or image. For instance, a word like “dog” could have dimensions representing size, friendliness, and color. Similarly, in image recognition, vectors denote different aspects like color or texture.
🌅 Vector Embeddings in Image Processing
Imagine an image of a vibrant sunset; to a computer, it’s not just a blend of colors but a combination of hues, textures, and shapes. Vector embeddings dissect these elements, breaking down the sunset into its individual parts. Each aspect, from the warm orange tones to the soft textures of clouds, is represented as a distinct numerical value within the vector.
This dissection is the key to a number of ML applications in image processing, such as object recognition, face detection algorithms, image search engines, and many more.
Check out the article above for more info regarding image processing if you’re interested.
💬 Vector Embeddings in NLP
In the realm of Natural Language Processing (NLP), vector embeddings decode the subtle nuances of words and sentences, transforming language into a format machines can comprehend. Given a word, such as “Charlie”, transforming it from an array of characters into a vector of floating-point numbers, you can see how similar or different it is to the other words in the vector space, such as “tower” and “Mary”.
These numerical patterns unlock the doors to a myriad of applications. Just to name a few:
- Sentiment analysis
- Machine translation
- Chatbots and virtual assistants
If you are interested to read more about NLP, you can visit this article.
