Pruning
Pruning is a technique used in machine learning and deep learning to reduce the size and complexity of neural networks by removing unnecessary or redundant parameters, such as weights, neurons, or connections. It aims to improve model efficiency, reduce computational costs, and mitigate overfitting while maintaining or even enhancing performance. This process is commonly applied in model optimization, especially for deployment on resource-constrained devices like mobile phones or edge devices.
Developers should learn pruning when working on deep learning projects that require efficient models for real-time inference, low-memory environments, or edge computing, as it helps reduce model size and latency without significant accuracy loss. It is particularly useful in scenarios like deploying AI on smartphones, IoT devices, or in production systems where computational resources are limited, and it can be combined with other techniques like quantization for further optimization.