# Overfitting and Regularization
Status: public
Confidence: medium (0.78) (verified)
Last verified: 2026-05-28
Generation: ai_structured


## TL;DR

Overfitting is the gap between fitting the training data and generalizing to new data. Regularization methods such as dropout, weight penalties, augmentation, and label smoothing are intended to improve generalization.

## Core Explanation

This repair pass removed unsupported survey metadata and retained only claims that map directly to the Deep Learning textbook, the JMLR dropout paper, and the Inception-v3 label-smoothing paper.

## Further Reading

- [Deep Learning - Chapter 7, Regularization for Deep Learning](https://www.deeplearningbook.org/contents/regularization.html)
- [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](https://jmlr.org/papers/v15/srivastava14a.html)
- [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567)

## Related Articles

- [Neural Network Basics](../neural-network-basics.md)
- [Activation Functions in Neural Networks](../activation-functions.md)
- [Backpropagation: The Engine of Neural Network Learning](../backpropagation.md)