# Learning with Limited Data

Good machine learning is heavily dependent on good data. [A few more good data-points is likely to be worth billions of model parameters.](https://arxiv.org/abs/2110.04374)

However, sometimes we need to train models when data is limited. There are a number of strategies that we can try.

### Zero-Shot and Few-Shot Learning

- [Pattern Exploitative Training](https://wiki.jamesravey.me/books/ai-and-ml/page/pattern-exploitative-training "Pattern Exploitative Training") is a way to use a small number of examples to train text classifiers. It is technically an example of synthetic data generation.

### In Context Learning (ICL)

### Synthetic Data Generation and Augmentation