Unsupervised deep learning is a category of algorithms that train models on a dataset without labeled responses. Unlike supervised learning, where you need labeled data, unsupervised learning seeks to identify complex patterns and relationships in the data without explicit guidance.

Here’s a basic overview of unsupervised deep learning:

1. Clustering: Grouping data into clusters based on similar features or characteristics. This is useful for discovering hidden patterns in data.
2. Dimensionality Reduction: This technique reduces the number of input variables in a dataset. It can be used to visualize high-dimensional data and is often used in conjunction with clustering techniques like PCA (Principal Component Analysis).
3. Generative Models: Generative models like GANs (Generative Adversarial Networks) and autoencoders can learn the underlying data distribution and generate new data points that are similar to the training data.
4. Anomaly Detection: Unsupervised learning can be used to detect anomalies or outliers in data, which can be critical in fields like fraud detection or quality control.
5. Feature Learning: Learning a good representation of the data without supervision can lead to valuable insights and can be used in various downstream tasks.

Unsupervised deep learning is a powerful tool that offers a lot of potentials, especially when labeled data is scarce or expensive to obtain. It can be applied to a wide range of tasks including natural language processing, image recognition, speech recognition, and more.