Contribution on the topic. (#31767)
* Contribution on the topic. Added some basic information on the concept of Multi Layer Perceptron. Added an image for better understanding of the concept. * Added extra information. Check out the following piece of data on MLP. * Update index.md * Update index.md
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Quincy Larson
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@ -3,13 +3,12 @@ title: Multi Layer Perceptron
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## Multi Layer Perceptron
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## Multi Layer Perceptron
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This is a stub. <a href='https://github.com/freecodecamp/guides/tree/master/src/pages/machine-learning/neural-networks/multi-layer-perceptron/index.md' target='_blank' rel='nofollow'>Help our community expand it</a>.
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Multi Layer Perceptron is a type of feed-forward neural network, consisting of many naurons. The layer is essentially dicided into three parts: an Input Layer, the Hidden Layers and the Output Layer. Here is an image of a simple MLP:
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<a href='https://github.com/freecodecamp/guides/blob/master/README.md' target='_blank' rel='nofollow'>This quick style guide will help ensure your pull request gets accepted</a>.
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<!-- The article goes here, in GitHub-flavored Markdown. Feel free to add YouTube videos, images, and CodePen/JSBin embeds -->
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Here, you can see that the MLP consists of an Input Layer with 3 neurons, then a single Hidden Layer with 4 neurons and finally a Output Layer with 2 neurons. Thus, the network, essentially, takes three values as input and outputs two values.
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The weights and the biases of each layer are initialised with random values and through a no of training on a given data, the values are adjusted, using backpropagation, to attain maximum accuracy in the output.
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#### More Information:
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### More Information:
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<!-- Please add any articles you think might be helpful to read before writing the article -->
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