APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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input
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weight
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net input function
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activation function
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output
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Detailed explanation-1: -An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations.
Detailed explanation-2: -In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function.
Detailed explanation-3: -If you do not use any activation function in a neural network, it would become a giant linear regression model as (X*W1*W2*W3) + (b1*W2*W3 + b2*W3 + b3) that can be simplified into (X*W) + B. Moreover, the hidden layers would be useless and the model will not learn any non-linear relationship in the data.
Detailed explanation-4: -A simple perceptron. Each input is connected to the neuron, shown in gray. Each connection has a weight, the value of which evolves over time, and is used to modify the input. Weighted inputs are summed, and this sum determines the output of the neuron, which is a classification (in this case, either 0 or 1).