This post delves into deep model pruning, distillation, and quantization techniques that help address the challenges posed by increasing complexity and resource demands of modern neural networks. These methods aim to reduce model size and improve efficiency, enabling deployment on a wide range of devices and opening up possibilities for real-world applications across various domains. The post covers the principles behind deep model pruning, distillation, and quantization in detail, outlines the steps of the processes, and discusses the trade-offs involved.