# 10 - Data Augmentation and Synthetic Data Generation: Growing Datasets

Expand limited data: augmentation for images (rotation, flip, zoom), text (back-translation, paraphrase), time series. SMOTE for imbalanced classification. Generative models (GANs, VAE) for synthetic data. Trade-off: diversity vs realism. When it helps, when it overfits.

## What you'll learn

- Geometric transformations: rotation, scaling, flip matrices - the basis of image augmentation
- Mixup: \\tilde{x} = \\lambda x_i + (1-\\lambda) x_j - regularization through interpolation
- SMOTE: interpolates between minority class neighbors to balance
- GAN: min-max game to generate realistic synthetic data
- VAE: reconstruction + KL divergence for a sampleable latent space

*This article is part of the **Math for AI** series on federicocalo.dev.*

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## Read the full article

The complete article (15 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [10 - Data Augmentation and Synthetic Data Generation: Growing Datasets](https://federicocalo.dev/en/blog/data-augmentation-synthetic-data-generation-datasets)**

`https://federicocalo.dev/en/blog/data-augmentation-synthetic-data-generation-datasets`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*