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<AI : Neural Network for beginners (Part 1 of 3) ⁄ >

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This article is part 1 of a series of 3 articles that I am going to post. The proposed article content will be as follows:

1. Part 1 : This one, will be an introduction into Perceptron networks (single layer neural networks)
2. Part 2 : Will be about multi layer neural networks, and the back propogation training method to solve a non linear classification problem such as the logic of an XOR logic gate. This is something that a Perceptron can't do. This is explained further within this article
3. Part 3 : Will be about how to use a genetic algorithm (GA) to train a multi layer neural network to solve some logic problem

Lets start with some biology

Nerve cells in the brain are called neurons, there are an estimated 1010 to the power(1013) neurons in the human brain, each neuron can make contact with up to several thousand other neurons. Neurons are the unit in the brain to process information.
So what does a neuron look like

A neuron consists of a cell body, with various extensions from it. Most of these are branches called dendrites. There is one much longer process (possibly also branching) called the axon The dashed line shows the axon hillock, where transmission of signals starts

The following diagram illustrates this.

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