What are embeddings in the context of neural networks?

Embeddings in neural networks are a method to represent discrete, high-dimensional data—like words, categories, or IDs—as continuous, low-dimensional vectors. These vectors capture meaningful relationships between the original data points by placing semantically similar items closer together in the vector space. For example, in natural language processing (NLP), the word “cat” might be represented as a 300-dimensional vector, and “dog” as another vector nearby, reflecting their similar meanings. Embeddings are learned during training, allowing the model to discover patterns that aren’t explicitly defined, making them essential for tasks like language modeling, recommendation systems, or handling categorical features.

Embeddings are created by training a neural network to map input data to a dense vector representation. During training, the model adjusts these vectors to minimize a loss function, such as predicting a target word from its context (as in Word2Vec) or classifying user-item interactions in recommendations. For instance, in a movie recommendation system, each user and movie might be assigned an embedding. The model learns to place users and movies they interact with closer in the embedding space. The dimensionality of the embedding (e.g., 50, 100, 300) is a hyperparameter: smaller dimensions may lose nuance but reduce computational cost, while larger ones capture more detail but risk overfitting. Embeddings are often initialized randomly and refined through backpropagation.

Practically, embeddings are implemented as lookup tables. In frameworks like PyTorch or TensorFlow, an embedding layer is a matrix where each row corresponds to a unique input identifier. For example, a vocabulary of 10,000 words with 300-dimensional embeddings would use a 10,000x300 matrix. During training, when the input word “apple” (ID 42) is passed, the layer retrieves the 42nd row of the matrix as its vector. Developers must handle out-of-vocabulary items (e.g., via hashing or default vectors) and decide whether to use pre-trained embeddings (like GloVe for NLP) or train them from scratch. Embeddings reduce computational complexity compared to one-hot encoding—especially for large vocabularies—and enable models to generalize better by sharing learned patterns across similar inputs.