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Data Augmentation

AI/ML Fundamentals

Creating variations of training data

What is Data Augmentation?

Artificially expanding dataset by applying transformations: rotation, flipping, cropping, color changes.

Real-World Examples

  • •Flipping images horizontally
  • •Adding noise to audio
  • •Paraphrasing text

When to Use This

When training data is limited, especially for image/audio tasks

Related Terms

Learn more about concepts related to Data Augmentation

Training

Process of teaching an ML model using data

Overfitting

Model learns training data too well, performs poorly on new data

Regularization

Techniques to prevent overfitting