👉 Reductions computing is a technique used in machine learning and deep learning to simplify complex models by reducing their dimensionality or complexity while preserving essential information. This is typically achieved through methods like feature selection, where less relevant features are removed, or through model compression techniques such as weight pruning and quantization, which reduce the size and computational requirements of neural networks. By doing so, reductions computing aims to enhance model efficiency, reduce inference time, and lower memory usage, making models more practical for deployment in resource-constrained environments without significantly compromising performance.
