Embeddings

Embeddings are a way to represent words, phrases, or even entire documents as numerical vectors in a high-dimensional space. The goal is to capture the semantic meaning of the text, so that words with similar meanings are located close to each other in the vector space. This allows machine learning models to understand relationships between words and perform tasks like text classification, sentiment analysis, and machine translation more effectively.

Visit the following resources to learn more:

• @article@Getting Started With Embeddings
• @article@A Guide on Word Embeddings in NLP