Data augmentation is a technique used to increase the diversity of data available for training machine learning models without actually collecting new data. It involves creating modified versions of existing data points, such as rotating or flipping images.
- Glossary > Letter: D
What does "Data Augmentation" mean?

Use Cases
Image Classification:
Enhancing the training dataset by creating variations of existing images.
Natural Language Processing:
Generating paraphrases of text data to improve model robustness.
Speech Recognition:
Adding noise to audio data to make models more resilient to variations.

Importance
Improves Generalization:
Helps models perform better on unseen data by exposing them to a wider variety of training examples.
Reduces Overfitting:
Prevents models from memorizing the training data by introducing variability.
Cost-Effective:
Enhances datasets without the need for expensive data collection processes.

Analogies
Data augmentation is like practising different variations of a dance routine. By rehearsing with slight changes in speed, direction, or style, dancers become more adaptable and capable of performing well under various conditions
Where can you find this term?
Ready to experience the full capabilities of the latest AI-driven solutions?
Contact us today to maximize your business’s potential!