👉 Fluid, in the context of neural networks and machine learning, refers to the continuous input data that models process to generate outputs. These inputs can take various forms, such as text, images, audio, or time-series data. Unlike discrete inputs like integers or categorical labels, fluid inputs are inherently continuous, allowing models to capture nuanced patterns and relationships within the data. For example, in natural language processing, fluid inputs might be sequences of words or characters, while in computer vision, they could be pixel values from an image. The continuous nature of fluid inputs enables models to perform complex tasks, like understanding language or recognizing patterns in visual data, by leveraging mathematical operations and algorithms designed to handle such inputs effectively.
