Description: Regularization techniques are methods used in machine learning to prevent overfitting, which occurs when a model fits too closely to the training data and loses its ability to generalize to new data. These techniques add a penalty to the model’s loss function, helping to keep the model’s complexity in check. There are various forms of regularization, such as L1 (Lasso) and L2 (Ridge), which are applied to the model’s coefficients, promoting simpler solutions and preventing some parameters from becoming excessively large. Regularization can also improve the quality of generated samples by preventing the model from memorizing the training data. In hyperparameter optimization, regularization becomes a crucial factor in finding the right balance between model complexity and performance. In unsupervised learning, regularization techniques can enhance the robustness of models when extracting meaningful features from data. Finally, in Generative Adversarial Networks (GANs), regularization is essential for stabilizing training and avoiding issues like mode collapse, where the generator produces a limited number of different samples. In summary, regularization techniques are fundamental for building more robust and generalizable models across various machine learning applications.
