Description: Network pruning is a technique used to reduce the size of a neural network by removing weights that are considered unnecessary. This process not only helps decrease the model’s complexity but can also improve its performance by reducing overfitting. Pruning is based on the premise that not all weights in a neural network are equally important; some can be removed without significantly affecting the model’s ability to generalize. The technique can be carried out in various ways, such as individual weight pruning, where weights close to zero are eliminated, or structural pruning, which involves removing entire neurons or layers. Network pruning is particularly relevant in the context of resource-constrained devices, such as mobile phones or IoT devices, where model efficiency and size are critical. Additionally, this technique has been integrated into various deep learning frameworks, facilitating its implementation in machine learning projects. In summary, network pruning is a valuable tool for optimizing deep learning models, allowing them to be lighter and faster without sacrificing performance.
