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I know about the Gradient Descent & Back-propagation Theorem. What I didn't get is: When and how to use Bias? Ex - When mapping the AND function, when I use 2 inputs and 1 output, it does not give the correct weights, however, when I use 3 inputs (1 of which is a bias), it gives the correct weights. Select the correct answer from above options

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Bias is just like an intercept added in a linear equation. It is an additional parameter in the Neural Network which is used to adjust the output along with the weighted sum of the inputs to the neuron. Moreover, bias value allows you to shift the activation function to either right or left. output = sum (weights * inputs) + bias The output is calculated by multiplying the inputs with their weights and then passing it through an activation function like the Sigmoid function, etc. Here, bias acts like a constant which helps the model to fit the given data. The steepness of the Sigmoid depends on the weight of the inputs. A simpler way to understand bias is through a constant c of a linear function y =mx + c It allows you to move the line down and up fitting the prediction with the data better. If the constant c is absent then the line will pass through the origin (0, 0) and you will get a poorer fit. Neural Network is an important component of Machine Learning. You can go through this Machine Learning Course and become a Machine Learning expert.

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