👉 Exponential Embeddings, or ee math, is a technique in natural language processing that maps words or phrases into dense vector spaces where semantically similar terms are positioned closer together. This is achieved through a combination of mathematical operations, primarily involving exponential functions and neural networks. The process starts by converting text into numerical vectors using word embeddings, then applying a layer of neural networks that uses an exponential activation function to transform these vectors. This transformation captures the context and meaning of words, allowing models to understand nuanced relationships between terms, such as synonyms or analogies. The result is a continuous, high-dimensional space where mathematical operations can be performed to infer relationships and perform tasks like similarity measurement or clustering, making ee math a powerful tool for semantic analysis in NLP.
