The embedding layer in a neural network is a trainable layer that converts discrete inputs, such as words or tokens, into dense vector representations (embeddings) that can be processed by subsequent layers. This layer acts as a bridge between raw input data and the model's hidden layers.
For example, in NLP tasks, the embedding layer maps each word or token in a vocabulary to a dense vector of fixed size. These vectors capture semantic relationships, enabling the model to learn patterns and structures in the data. The embedding layer is often initialized with pre-trained embeddings (like Word2Vec or GloVe) and can be fine-tuned during training to adapt to the specific task.
In addition to text data, embedding layers are also used for categorical features in structured data, such as user IDs or product categories, transforming them into meaningful numerical representations. This layer is essential for reducing the dimensionality of inputs and improving the efficiency of neural network training.