What is the relationship between embeddings and neural networks?

Embeddings are low-dimensional, continuous vector representations of discrete or high-dimensional data, and they play a key role in how neural networks process complex inputs like text, categories, or relationships. Neural networks often struggle with raw, unstructured data (e.g., words or user IDs) because these inputs lack inherent numerical meaning. Embeddings solve this by mapping such data into a dense, compact vector space where similar items are closer together. This mapping is typically learned automatically by the neural network during training, allowing the model to capture semantic or contextual relationships in the data.

For example, in natural language processing (NLP), words are converted into embeddings before being fed into a neural network. Without embeddings, a word like “cat” might be represented as a one-hot encoded vector with thousands of dimensions (e.g., [0,0,…,1,…,0]), which is computationally inefficient and lacks meaningful relationships between words. Instead, an embedding layer in a neural network projects each word into a dense vector (e.g., 300 dimensions) where similar words like “cat” and “dog” have vectors that are geometrically closer. This enables the network to generalize better and understand context. Similarly, in recommendation systems, user and item IDs are embedded to capture latent features (e.g., user preferences or product characteristics) that a neural network can use to predict interactions.

Embeddings are tightly integrated into neural network architectures. They are often implemented as a trainable layer (e.g., nn.Embedding in PyTorch or Embedding in Keras) that converts discrete tokens into vectors. During training, the network adjusts these embeddings through backpropagation, refining their values to minimize prediction errors. For instance, in a text classification model, the embedding layer learns to position words with similar meanings or functions near each other in the vector space, which improves the model’s ability to recognize patterns. This approach is not limited to text: embeddings are also used for categorical features in tabular data, graph nodes in graph neural networks, or even images in vision transformers. By compressing and structuring data into meaningful representations, embeddings enable neural networks to handle diverse inputs efficiently and effectively.