Machine Learning

  NN : Forward and Back Propagation Q1. Sigmoid and softmax functions Which of the following statements is true for a neural network having more than one output neuron ? Choose the correct answer from below: A.     In a neural network where the output neurons have the sigmoid activation, the sum of all the outputs from the neurons is always 1. B.     In a neural network where the output neurons have the sigmoid activation, the sum of all the outputs from the neurons is 1 if and only if we have just two output neurons. C.     In a neural network where the output neurons have the softmax activation, the sum of all the outputs from the neurons is always 1. D.     The softmax function is a special case of the sigmoid function Ans: C Correct option :  In a neural network where the output neurons have the softmax activation, the sum of all the outputs from the neurons is always 1. Explanation : For the sigmoid activation, when we have more than one neuron, it is

  Q1. Complete the code For the above code implementation of forward and backward propagation for the sigmoid function, complete the backward pass [ ???? ] to compute analytical gradients. Note:  grad in backward is actually the output error gradients. Choose the correct answer from below: A.      grad_input = self.sig * (1-self.sig) * grad B.      grad_input = self.sig / (1-self.sig) * grad C.       grad_input = self.sig / (1-self.sig) + grad D.      grad_input = self.sig + (1-self.sig) - grad Ans: A Correct Answer :  grad_input = self.sig * (1-self.sig) * grad Explanation :  The  grad_input  will be given by : dZ  = The error introduced by input Z. dA  = The error introduced by output A. σ(x) · 1 − σ(x)  = The derivative of the Sigmoid activation function. where σ(x) represents the sigmoid function. Q2. Trained Perceptron A perceptron was trained to distinguish between two classes, "+1" and "-1". The result is