What is the difference between compression and regularization in machine learning?

What is the difference between compression and regularization in machine learning?

Compression refers to reducing the size of a model or dataset, while regularization helps prevent overfitting by adding constraints.

Can you give me examples of each?

Sure! Compression can be techniques like quantization or pruning, while regularization examples include L1 and L2 regularization.

How do these techniques affect model performance?

Compression can speed up inference and reduce memory usage, while regularization improves generalization to unseen data.

Are there any trade-offs I should be aware of?

Yes, excessive compression might lead to loss of important information, and too much regularization can underfit the model.