Tag Archives: Machine Learning
Regularization Techniques to Improve Model Generalization
Introduction In our last discussion, we explored dropout regularization techniques, which involve randomly setting a fraction of the activations to zero during training. This helps prevent overfitting by encouraging the network to learn redundant representations and improving generalization. Today, we will extend our focus to other regularization methods, including L1 and L2 regularization, label smoothing,…
Mitigating Overfitting with Ridge Regression: A Step-by-Step Guide Using Polynomial Regression
Introduction One of the simplest ways to simulate overfitting is to use polynomial regression on a small dataset. We can fit a high-degree polynomial to a small dataset, which will lead to overfitting. Then we can see how regularization techniques like Ridge Regression (L2 regularization) help to mitigate the overfitting. Step 1: Generate a Small…