Activation Functions in Neural Networks

Status: public · Confidence: medium (0.78) · Basis: verified_sources

## TL;DR

Activation functions introduce nonlinear transformations into neural networks. Without nonlinearities, stacked linear layers collapse into a single linear mapping.

## Core Explanation

ReLU is a simple piecewise-linear activation widely used in hidden layers. GELU and Swish are smoother nonlinearities used in many modern architectures. Softmax is commonly used where a model needs a normalized distribution over classes or tokens.

## Detailed Analysis

The practical choice of activation function depends on architecture, optimization behavior, and output semantics. ReLU variants are simple and fast; GELU and Swish can improve performance in some deep models; softmax is usually reserved for output distributions or attention weights rather than hidden-layer feature extraction.

## Further Reading

- [Deep Learning](https://www.deeplearningbook.org/contents/mlp.html)
- [Rectified Linear Units Improve Restricted Boltzmann Machines](https://www.cs.toronto.edu/~hinton/absps/reluICML.pdf)
- [Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415)
- [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)

## Related Articles

- [Kolmogorov-Arnold Networks (KANs): Learnable Activation Functions as MLP Alternatives](../kolmogorov-arnold-networks.md)
- [AI for Fraud Detection: Graph Neural Networks, Anti-Money Laundering, and Financial Crime](../ai-for-fraud-detection.md)
- [Convolutional Neural Networks (CNN)](../convolutional-neural-networks-cnn.md)