Embeddings are generated by training a deep learning model to map high-dimensional data (like text, images, or categories) into a lower-dimensional vector space. This is done by designing a neural network architecture that processes input data through layers of transformations, adjusting its parameters during training to capture meaningful patterns. For example, in natural language processing (NLP), models like Word2Vec or BERT convert words or sentences into vectors by analyzing their context in large text corpora. The model learns to position similar items (e.g., words with related meanings) closer together in the vector space, creating a structured representation.
A common approach involves using a neural network with an embedding layer. This layer acts as a lookup table that maps discrete inputs (like word IDs or user IDs) to dense vectors. During training, the model optimizes these vectors by minimizing a loss function. For instance, in a recommendation system, the model might learn user and item embeddings by predicting user-item interactions. In computer vision, convolutional neural networks (CNNs) generate image embeddings by passing pixels through layers that progressively extract features (edges, textures, objects), with the final layer before classification serving as the embedding. Transformers, used in models like GPT or ViT, create embeddings by processing sequences or patches through self-attention mechanisms, capturing relationships between elements.
Developers can generate embeddings using frameworks like TensorFlow or PyTorch. For example, in PyTorch, an embedding layer (nn.Embedding) initializes random vectors and updates them via backpropagation. Pre-trained models (e.g., ResNet for images, BERT for text) allow extracting embeddings without training from scratch by using intermediate layer outputs. Fine-tuning these models on domain-specific data adapts the embeddings to new tasks, like sentiment analysis. Key considerations include choosing the right architecture, layer depth (e.g., using the last hidden layer), and normalization (e.g., L2-normalizing vectors for cosine similarity). Embeddings simplify downstream tasks by converting raw data into compact, semantically rich representations that algorithms can process efficiently.