The Use of Regularization in Deep Learning Neural Networks

This article provides a detailed introduction to three commonly used regularization techniques in deep learning: L2 regularization, Dropout, and a 3-layer network model with regularization. It also enhances the performance of neural networks on the MNIST dataset by implementing these methods. The article includes step-by-step explanations of the code and result analysis. ### Summary of Main Content #### Model Introduction The article first introduces three common regularization techniques: 1. **L2-Regularization**: Reduces model complexity by penalizing weights. 2. **Dropout**: By randomly deactivating