How do embedding techniques work?

Can you explain how embedding techniques work in AI?

Sure! Embedding techniques transform categorical data into continuous vector spaces, making it easier for algorithms to process.

What are some common types of embedding techniques?

Common types include Word2Vec, GloVe, and sentence embeddings like Universal Sentence Encoder.

How do these embeddings improve machine learning models?

They capture semantic relationships and contextual meanings, enhancing the model's ability to understand and predict.

Can you give an example of how Word2Vec works?

Certainly! Word2Vec uses neural networks to learn word associations from a large corpus, creating vector representations.