👉 Captured math, often used in machine learning and deep learning contexts, refers to the process of extracting useful mathematical representations or patterns from large datasets. This involves training models to learn complex functions that can approximate the underlying structure of the data, such as functions from inputs to outputs. By capturing these mathematical relationships, models can generalize well to new, unseen data, making predictions or decisions with high accuracy. Techniques like neural networks, kernel methods, and autoencoders are commonly used to achieve this, enabling the transformation of raw data into a form that captures essential features and dependencies, which can then be used for tasks like classification, regression, or generative modeling.
