# Neural Networks and the Backpropagation Algorithm

DevFeed: [Neural Networks and the Backpropagation Algorithm](<https://devfeed.tech/articles/neural-networks-and-the-backpropagation-algorithm-40296.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2012/12/09/neural-networks-and-backpropagation/>)

Published: 2012-12-09T21:24:48Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [classes](<https://devfeed.tech/tags/classes.md>), [data](<https://devfeed.tech/tags/data.md>), [gradient-descent](<https://devfeed.tech/tags/gradient-descent.md>), [graph-theory](<https://devfeed.tech/tags/graph-theory.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [perceptron](<https://devfeed.tech/tags/perceptron.md>)

## AI overview

This tutorial introduces neurons as an extension of the perceptron model. It explains how multiple perceptron models can introduce nonlinearity into classification and begins defining a neuron using weighted inputs, an inner product, and a bias term.

## Source excerpt

Neurons, as an Extension of the Perceptron Model In a previous post in this series we investigated the Perceptron model for determining whether some data was linearly separable. That is, given a data set where the points are labelled in one of two classes, we were interested in finding a hyperplane that separates the classes. In the case of points in the plane, this just reduced to finding lines which separated the points like this: