data-augmentation

Vocabulary Word

Definition

'Data augmentation' is a technique to generate more data for your study or project. It's like taking a photo and then creating more - like flipped, zoomed-in, or slightly rotated versions of the original.

Examples in Different Contexts

In machine learning, 'data augmentation' is a technique used to increase the diversity of training data by applying various transformations, such as flipping, rotation, or adding noise. A data scientist might say, 'Data augmentation is essential for improving the robustness of our image recognition models by exposing them to a wider variety of scenarios.'

Practice Scenarios

Tech

Scenario:

The current dataset doesn't have enough variations. For better performance, we may have to consider some enhancements.

Response:

Agreed. To improve the dataset variability, we can use data augmentation methods. This can generate the variations we need.

Scenario:

The bot seems to predict the sequence quite accurately, which may be due to overfitting. We should explore ways to introduce more randomness.

Response:

That's a good point. Implementing data augmentation techniques can help us introduce more randomness in the training data.