Neural networks play a central role in generating embeddings by learning the representations of data in a continuous vector space. In tasks like natural language processing, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to process the input data (e.g., text or images) and extract features that are useful for creating embeddings. For example, in word embeddings, a neural network is trained to predict context words given a target word. This training process adjusts the network's parameters, allowing it to generate high-quality embeddings that capture the relationships between words.
Neural networks are capable of capturing complex patterns in data that simpler models might miss. By passing data through multiple layers of the network, the model can learn hierarchical features, where each layer captures increasingly abstract representations. In the case of word embeddings, the model learns relationships like synonyms, antonyms, and context-dependent meanings by adjusting the weights of the network.
The network-based approach allows embeddings to be learned in an unsupervised manner, which means they can be trained without explicit labels. Neural networks use large amounts of data to adjust the weights in such a way that similar inputs are mapped to nearby points in the embedding space, making the embeddings useful for downstream tasks like classification, clustering, or retrieval.