What is data augmentation and how can it improve my model?

What is data augmentation and how can it improve my model?

Data augmentation is a technique used to increase the diversity of your training dataset by applying various transformations to the existing data. This can help improve your model's performance by making it more robust to variations.

What are some common data augmentation techniques?

Common techniques include rotation, flipping, scaling, cropping, and adding noise to images. For text, you might use synonym replacement or back-translation.

How do I implement data augmentation in my project?

You can use libraries like TensorFlow or PyTorch, which have built-in functions for data augmentation. Just apply the transformations during the data loading process.

Can data augmentation lead to overfitting?

While data augmentation generally helps reduce overfitting by providing more varied data, if not done carefully, it can introduce noise that may confuse the model.