Embeddings are dense vector representations of discrete data, such as words, items, or categories, that capture semantic relationships between them. In neural networks, embeddings transform categorical data into continuous vectors in a lower-dimensional space, making it easier for the model to learn patterns and relationships.
For example, in natural language processing (NLP), word embeddings like Word2Vec or GloVe represent words as vectors, where similar words are closer together in the vector space. This allows the neural network to learn nuanced meanings and associations between words.
Embeddings are particularly useful when dealing with high-cardinality categorical data, such as text or products, as they enable more efficient and meaningful processing compared to traditional one-hot encoding.