parameter-norm-penalties

L1 Regularization

Machine learning algorithms like neural networks are used to make predictions based on input data. These algorithms use weights, which are values assigned to inputs, to make these predictions. Overfitting is a common problem in machine learning, where the algorithm becomes too complex and begins to fit to noise rather than the actual data. This results in poor performance on new, unseen data. Regularization techniques help to prevent overfitting by limiting the complexity of the model. One such

Weight Decay

Overview of Weight Decay In deep learning, the weight parameters in a neural network can grow very large if left unchecked. This often results in overfitting the model to the training data, which leads to poor performance on new data. To prevent this from happening, regularization techniques, such as weight decay, are used. Weight decay is also known as $L_{2}$ regularization because it involves adding a penalty on the $L_{2}$ norm of the weights to the original loss function. What is Weight

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