Multi-layer Perceptron Neural Network || Lesson 4 || Deep Learning || Learning Monkey ||

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In this class we discuss multilayered Perceptron in neural network.
The discussion about single Perceptron is done in our previous class.
In multilayered network first one is considered as input layer.
Next layer we take neurons and this layer we call it first hidden layer.
Input is send to each neuron in the first hidden layer .
We randomly select weights and the input is multiplied with weights and the summation value is taken as output to the neuron.
These outputs are taken as input to second hidden layer.
Here in each neuron we apply activation function. Whats the need of activation function we understand in next classes.
The last layer we call it as output layer.
The output got from last layer is considered as predicted output and loss function applied on the output.


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