```dataview LIST WITHOUT ID file.link WHERE file.cday = date(this.file.name) SORT file.cday ASC ``` Here's a breakdown: 1. **Not Random:** Real-world data isn't just random noise. Images of faces, for example, follow specific rules (two eyes generally appear above a nose, which is above a mouth, all within a certain head shape). Sounds that constitute speech or music also have complex, non-random patterns. 2. **Patterns and Constraints:** This "structure" encompasses all these implicit rules, typical features, and ways data points relate to each other. For images, it includes things like typical shapes, textures, colour combinations, and how objects are usually arranged. 3. **Data Distribution:** This refers to how the data points are spread out in their high-dimensional space (e.g., pixel space for images). The "manifold hypothesis" discussed in the text suggests this distribution isn't uniform but is highly _concentrated_ in specific regions (the manifolds) that correspond to meaningful data. 4. **Capturing [[the Structure]]:** The "continuous [[latent space]]" learned by a deep generative model (like a GAN or VAE) through manifold learning is designed to be a lower-dimensional representation that effectively _captures_ or _models_ this underlying structure. When the model generates a new sample by picking a point in this [[latent space]], it's essentially drawing from this learned structure, ensuring the output conforms to the patterns and constraints of the original data (e.g., generating a realistic-looking face rather than random pixels).