Data augmentation2 Common regularization techniques in ML - Data augmentation Data augmentation is a technique used in machine learning and computer vision to artificially increase the size and diversity of the training dataset by applying a set of transformations to the existing data. The main goal of data augmentation is to introduce variations in the training data so that the model can learn and generalize better. By generating additional examples, data augmentation he.. 2023. 8. 2. Common regularization techniques in machine learning Regularization is a technique in machine learning that aims to prevent overfitting by adding a penalty term to the loss function. This penalty term discourages complex models that fit the training data too well but may not generalize well to unseen data. Some common regularization techniques in machine learning include: 1. L1 and L2 Regularization: L1 regularization, also known as Lasso regressi.. 2023. 8. 2. 이전 1 다음