A Beginner’s Guide to Neural Network Pruning

Deep neural networks have been responsible for much of the advances in machine learning over the last decade. Many of these networks, especially the best-performing ones, necessitate massive amounts of computing and memory. These restrictions not only raise infrastructure costs but also complicate network implementation in resource-constrained contexts like mobile phones and smart devices.  Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time. The goal of neural network pruning is to convert a large network to a smaller network with equivalent accuracy. Here in this article, we will discuss the important points related to neural network pruning. The major poi