Chapter 7: Regularization and Generalization with PyTorch
Abstract : Regularization and generalization are crucial concepts in machine learning, particularly when training neural networks with PyTorch. Regularization techniques aim to prevent overfitting and improve the model's ability to generalize to unseen data. Generalization refers to the model's performance on data it has not encountered during training. Here's how regularization and generalization are addressed in PyTorch: 1. Regularization Techniques in PyTorch: L1 and L2 Regularization (Weight Decay): L2 regularization, often referred to as weight decay, adds a penalty to the loss function proportional to the square of the weights. This encourages smaller weights, leading to simpler models and reducing overfitting. In PyTorch, L2 regularization is typically applied by setting the weight_decay parameter in the optimizer (e.g., torch.optim.Adam or torch.optim.SGD ). L1 regularization adds a penalty proportional to the ...