Think Different

                         Photo by Karolina Grabowska Embedding, in the context of machine learning and natural language processing, refers to the representation of objects, such as words or sentences, in a continuous vector space. The goal of embedding is to capture semantic relationships, similarities, and contextual information between words or entities, making it easier for machine learning models to understand and process them. Here's a breakdown of embedding with examples,  categories, and context: Embeddings, in the realm of natural language processing, serve as numerical representations that gauge the interconnectedness of text strings. These embeddings find versatile applications, including: 1. Search: Ranking results based on their relevance to a given query string. 2. Clustering: Grouping text strings together based on their similarity. 3. Recommendations: Recommending items with tex...