Research and Application of Lightweight Technology of Convolutional Neural Networks in Deep Learning

This article summarizes the convolution in the deep learning of the neural network lightweight technology, including the background, technical comparison, classification, experimental design, technical implementation, and industry application case and future tendency. Through the analysis of the current research situation, the article points out the importance of lightweight technology for practical application. It details the network structure optimization and lightweight methods such as quantitative parameters and pruning. In the experimental part, the article selected the AlexNet, VGG, and ResNet classic model as the foundation, set up reasonable experimental parameters, and lightweight methods for the implementation and performance evaluation. The case analysis part of lightweight technology in the mobile terminal discusses the application of visual image recognition and automatic driving systems, as well as prospects for the future direction of technical progress and new application scenarios.