Description: Batch sampling is a method of selecting a subset of data points from a larger dataset in batches. This approach is fundamental in the field of machine learning, as it allows for more efficient handling of large volumes of data. Instead of processing the entire dataset at once, batch sampling divides the data into smaller groups, making it easier to train complex models without requiring excessive memory. This method also helps improve the convergence of optimization algorithms, as it allows for more frequent updates to the model parameters with each batch of data. In distributed learning contexts, batch sampling enables local devices to train models using only a portion of their data, reducing the need to send large amounts of information to a central server. In generative adversarial networks (GANs), batch sampling is used to train both the generator and discriminator more effectively, allowing both models to learn in a more balanced manner. In convolutional neural networks (CNNs), this approach is crucial for processing large image datasets more efficiently and effectively.
