adataugmentációval

Adataumentációval, known more formally as data augmentation, is a technique used in machine learning and deep learning to artificially increase the size of a training dataset. This is achieved by creating modified versions of existing data points. The goal of data augmentation is to improve the performance and generalization ability of machine learning models.

The process typically involves applying various transformations to the original data that preserve the class labels. For example, in image recognition, common augmentation techniques include random cropping, flipping, rotation, zooming, and color jittering. These transformations introduce variations that the model might encounter in real-world scenarios, making it more robust to these variations and less likely to overfit to the training data.

Data augmentation is particularly useful when the available dataset is small or unbalanced. By generating diverse synthetic data, it helps to expose the model to a wider range of examples without the need to collect and label new, real-world data. This can lead to more accurate predictions and a better understanding of the underlying patterns in the data. The specific augmentation techniques applied often depend on the type of data and the problem being addressed. For text data, augmentation might involve synonym replacement, sentence shuffling, or back-translation. For audio data, techniques like time stretching, pitch shifting, and adding background noise can be employed.