# perceptron

Published articles for perceptron.

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## 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:

## The Perceptron, and All the Things it Can't Perceive

DevFeed: [The Perceptron, and All the Things it Can't Perceive](<https://devfeed.tech/articles/the-perceptron-and-all-the-things-it-can-t-perceive-40238.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2011/08/11/the-perceptron-and-all-the-things-it-cant-perceive/>)

Published: 2011-08-11T13:16:32Z

Content type: tutorial

Language: en

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

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>)

Tags: [classification](<https://devfeed.tech/tags/classification.md>), [linear-algebra](<https://devfeed.tech/tags/linear-algebra.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [perceptron](<https://devfeed.tech/tags/perceptron.md>), [programming](<https://devfeed.tech/tags/programming.md>), [pseudocode](<https://devfeed.tech/tags/pseudocode.md>)

### AI overview

This tutorial introduces the machine learning classification problem and explains the perceptron as a model that separates labeled data with a line or hyperplane.

### Source excerpt

This post assumes some basic familiarity with Euclidean geometry and linear algebra. Though we do not assume so much knowledge as is contained in our primer on inner product spaces, we will be working with the real Euclidean inner product. For the purpose of this post, it suffices to know about the "dot product" of two vectors. The General Problem One of the main problems in machine learning is to classify data.