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* 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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Multi Layer Perceptron
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:
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. 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.