# tangent propagation

## Summary

Tangent propagation is a way of regularizing neural nets. It encourages the representation to be invariant by penalizing large changes in the representation when small transformations are applied to the inputs.

## Context

This concept has the prerequisites:

- backpropagation (Tangent propagation is based on the same idea as backpropagation.)
- learning invariances in neural nets (Tangent propagation is a technique for learning invariances in neural nets.)

## Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

## Supplemental resources (the following are optional, but you may find them useful)

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 5.5.4, pages 263-265

## See also

- Some other strategies for learning invariances:
- building it explicitly into the architecture, as in convolutional nets
- augmenting the training set with warped examples
- Tikhonov regularization , which penalizes instability with respect to noise