Understanding the Effects of Model Optimization Methods in Enhanced CNNs

In-depth examination of model optimization techniques and their effects on enhanced convolutional neural networks (CNNs) is done in this paper. The performance of Convolutional Neural Networks, which are the foundation of many computer vision applications, is greatly influenced by the optimization strategies used during training. The goal of this study is to have a thorough understanding of how different model optimization techniques affect CNN performance. In the context of Enhanced CNNs, the research assesses and contrasts well-known model optimization methods, such as Stochastic Gradient Descent (SGD), Adam, RMSprop, and others. This study's findings can help practitioners and academics make well-informed decisions regarding the best optimization approaches to optimize the effectiveness and efficiency of Enhanced CNNs in a variety of practical applications.