While neural networks primarily rely on labeled data for supervised learning, unsupervised learning techniques allow them to learn from unlabeled data as well. In unsupervised learning, the network identifies patterns and structures by finding relationships and similarities among the input data points. This can be useful for tasks like clustering, dimensionality reduction, and generative modeling.