I'm going to post some basics related to AI.
An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models.
First of all, to understand embedding, you need to know what a vector is in computer data.
Vectors are 1-dimentional Arrays
A vector can be represented as a matrix
This is the vector concept in computer data processing.
To put it simply, embedding is expressing data as a vector.
In other words, it is expressed as a determinant.
Embedding is the foundation of AI and is something that anyone pursuing AI should know. I hope this article will be of some help to beginners learning AI.
Top comments (3)
A great start indeed!!! I would love to see your explanations powered by some code snippets, links where beginners can find out more resources, some other examples. As Ranjan mentioned, Cohere docs have a really good and detailed explanation.
Great start.
Please refer to Cohere documentation for detailed understanding of embeddings with code sample.
Cohere-Embed
You can explain your ideas using bullet points and a numbered list.