{"id":200,"date":"2018-01-07T18:55:00","date_gmt":"2018-01-07T18:55:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/2018\/01\/07\/basics-of-multilayer-perceptron-a-simple-explanation-of-multilayer-perceptron\/"},"modified":"2018-01-07T18:55:00","modified_gmt":"2018-01-07T18:55:00","slug":"basics-of-multilayer-perceptron-a-simple-explanation-of-multilayer-perceptron","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/basics-of-multilayer-perceptron-a-simple-explanation-of-multilayer-perceptron\/","title":{"rendered":"Basics of Multilayer Perceptron – A Simple Explanation of Multilayer Perceptron"},"content":{"rendered":"
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Today we will understand the concept of Multilayer Perceptron.<\/p>\n

Recap of Perceptron <\/b>
You already know that the basic unit of a neural network is a network that has just a single node, and this is referred to as the perceptron<\/b>.
The perceptron is made up of inputs x1<\/sub>, x2<\/sub>, …, xn<\/sub> their corresponding weights w1<\/sub>, w2<\/sub>, …, wn<\/sub>. A function known as activation function takes these inputs, multiplies them with their corresponding weights and produces an output y.<\/p>\n\n\n\n\n
<\/a><\/td>\n<\/tr>\n
Figure 1: A Perceptron (‘single-layer’ perceptron)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n


<\/span>What is Multilayer Perceptron?<\/b><\/span>
A multilayer perceptron is a class of neural network that is made up of at least 3 nodes. So now you can see the difference. Also, each of the node of the multilayer perceptron, except the input node is a neuron that uses a non-linear activation function<\/i>.
The nodes of the multilayer perceptron are arranged in layers.<\/p>\n