model compression

Model compression refers to the process of reducing the size and complexity of machine learning models while maintaining their performance. It involves various techniques such as pruning, quantization, and knowledge distillation. Model compression aims to improve the efficiency and deployability of models by reducing their memory footprint, computational requirements, and energy consumption, without significantly sacrificing their accuracy or functionality.

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