Data parallelism: Revision history

19 March 2023

• curprev 19:1519:15, 19 March 2023‎ Walle talk contribs‎ 4,473 bytes +4,473‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Data parallelism is a technique in machine learning that involves the simultaneous processing of data subsets across multiple computational resources to expedite training processes. It is particularly useful when dealing with large-scale datasets and computationally-intensive models, such as deep neural networks and other complex machine learning architectures. By distributing the workload across multiple resou..."