An embedding layer in deep learning is a type of layer used to convert categorical variables, often words or tokens, into dense vector representations. These representations are learned during the training process and capture semantic meanings and relationships between the items. For example, in natural language processing (NLP), words that have similar meanings tend to have similar vector representations. This process allows models to handle high-dimensional categorical data more efficiently by transforming sparse data—like one-hot encoded vectors—into compact, continuous vectors.
The main benefit of using an embedding layer is that it reduces dimensionality and captures contextual relationships among inputs. Instead of representing each word as a unique one-hot vector, which can lead to a very high-dimensional and sparse representation, an embedding layer maps each word to a fixed-size, dense vector. For instance, in a 100-dimensional embedding space, each word might be represented as a point in that space, where the distance between points encodes the relationship between the words. Common implementations include the use of pre-trained embeddings like Word2Vec or GloVe, but an embedding layer can also learn these representations from scratch during the training of a neural network.
In practical terms, an embedding layer is often the first layer in models dealing with text or categorical inputs. For example, in a sentiment analysis model, you might input a sequence of words from a review. The embedding layer processes these words and maps them into a numerical format that the subsequent layers of the model can work with. By using an embedding layer, developers can ensure that their models not only understand the individual words but also the context and relationships between them, leading to better overall performance in tasks like classification or sequence prediction.