What is pruning?

In the context of machine learning, pruning refers to techniques that reduce the complexity of a model, typically by removing less important connections or parameters. This can lead to smaller, faster, and more energy-efficient models that are less prone to overfitting. Pruning is commonly used in deep learning to reduce the size of neural networks, making them more suitable for deployment on resource-constrained devices or for faster inference.