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Model compression refers to a set of techniques aimed at reducing the size and computational requirements of machine learning models while maintaining their performance. It enables deployment of models on resource-constrained devices and improves inference efficiency.
Neural network pruning is a technique in machine learning used to reduce the size and complexity of artificial neural networks by removing unnecessary parameters or connections. This process aims to improve efficiency, decrease computational requirements, and sometimes enhance generalization without significantly compromising accuracy.