👉 Sampling computing is a statistical method used in machine learning to approximate complex probability distributions with simpler, more manageable models. It involves selecting a subset of data points from the original dataset, known as a sample, rather than using the entire dataset. This technique is particularly useful when dealing with high-dimensional or large datasets, as it reduces computational complexity and memory requirements while still capturing the essential characteristics of the data distribution. By leveraging algorithms like stochastic gradient descent or variational inference, sampling computing enables efficient training of models such as generative adversarial networks (GANs) and variational autoencoders (VAEs), allowing for tasks like data generation, anomaly detection, and feature learning without the need to process the full dataset.
