Dividing a dataset into smaller, manageable subsets is a fundamental preprocessing step in various data-driven tasks. This process involves partitioning the overall data collection into multiple distinct groups, each containing a specific number of data points. For instance, a dataset of 1000 images might be divided into 10 subsets, each containing 100 images.
Employing this data handling strategy yields several advantages. It enables processing of large datasets that might exceed available memory limitations. Furthermore, it can accelerate computations by facilitating parallel processing of individual subsets. Historically, this approach has been crucial in training large models, enabling iterative updates based on smaller data portions.
