Max-Norm Regularization: Theory and Importance in Deep Learning Introduction Max-norm regularization is a weight constraint technique used in deep learning to prevent the weights of a neural network from growing too large. This method helps prevent overfitting by ensuring that the model doesn’t rely too heavily on specific features by excessively growing weights. Instead, max-norm regularization constrains the weight vector so that its size remains manageable, which stabilizes training and improves the model’s ability to generalize to new data. This technique is particularly useful in deep networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), where large weights can cause significant problems such as unstable gradients or overfitting during training. 1. Why Regularization is Needed in Neural Networks Neural networks are flexible models capable of learning complex relationships between inputs and outputs. However, this flexibility can lead to overfitting, where the model memorizes the training data rather than learning general patterns. One key reason for overfitting is the uncontrolled growth of large weights during training. When weights grow too large, the model becomes too sensitive to small variations in input, causing unstable predictions and poor generalization on unseen data. Regularization methods like max-norm regularization directly address this issue…
Learn Max-Norm Regularization to avoid overfitting : Theory and Importance in Deep Learning and proof – Day 49
