How can I use data augmentation in my machine learning projects?

How can I use data augmentation in my machine learning projects?

Data augmentation involves creating new training samples by modifying existing ones. This can help improve model performance.

What are some common techniques for data augmentation?

Common techniques include rotation, flipping, scaling, cropping, and adding noise to images.

Can data augmentation be used for text data as well?

Yes, for text data, techniques like synonym replacement, random insertion, and back-translation can be used.

How do I implement data augmentation in Python?

You can use libraries like TensorFlow or PyTorch, which have built-in functions for data augmentation.